Reading Response and Thinking about Research Questions Essay

Reading Response and Thinking about Research Questions - Essay Example The read essays from the book have different titles and authors. The first essay is written by Brian O’Leary and the title of the essay is â€Å"Tools of the Digital Workflow†. The essay basically talks about how the nature, history and business of publishers vary greatly, making it hard to identify a set if preferred tools. As the content changes to a more robust digital environment, publishers need to make decisions about related services that are licensed or outsourced completely. The second essay is entitled â€Å"Why the Book and the Internet Will Merge† and is written by Hugh McGuire. In summary, there is no much incentive for publishers to change books into something that can be read on a screen. The reason for this argument is that many would not prefer reading books from screens. However, most people prefer reading books in form of books and they equally prefer spending most of their time on the internet making funny photos of cats, blogging about what t hey have done, and contributing to the world’s biggest encyclopedia (McGuire & OLeary). In the first essay, â€Å"Tools of the Digital Workflow†, it is interesting to learn that digital workflows greatly helps publishers in thinking about product planning. Even though the practice is still rare, using digital workflows can signal the start of a period of discovering work. It therefore means that event-driven publishing can change into a more continuous model. In the second essay, â€Å"Why the Book and the Internet Will Merge†, it is interesting to learn that eBooks have arrived in the market in force. Unlike in 2008 when only 1% of trade book sales in the United States were eBooks, the number had hit 20% by 2011. With this steady increase in the number of eBooks in the market, the number in expected to hit 50% by the end of 2015 (McGuire &

Different Ethnic Groups Essay Example for Free

Different Ethnic Groups Essay After going through and being asked to react to the following statement, â€Å"Students who dine solely with members of their own ethnic group and participate in ethnic student organizations and activities contribute to a decline of ethnic relations on campus,† I have come to realized that as any other statements there’s always two sides and a lot more to consider before coming to a consent as a whole. In my opinion this is very important and should always view both sides of the story Many types of these ethnic group’s actions could make it easy and lead to a decline of ethnic relations on campus. For instance, let’s say if ethnic students did everything together as a whole and only participated in ethnic student activities, then it would clearly segregate the campus. Unfortunately no a days, this world for the most part is over the whole â€Å"whites and blacks racial dividedness† and no one should be seeking to head back on that path. Now, campus cafeterias could show a sign of being like that again with several different ethnic groups scattered around. If each ethnic group had its individual organization, I fell that the members could get extremely complacent and start to sense a feeling of superiority. This eventually could lead to conflict with other ethnic groups throughout the campus. It is very also important to look at how other students will view these ethnic groups. Other students may not like these groups, which once again could cause conflict. Another scenario, students may feel threatened by these groups, therefore making them not want to attend class or socialize around campus. There could also be a chance that these ethnic groups could try to bring down a disliked professor or even another group. Universities un-affiliated with a religion may deal with groups with strong religious beliefs and could try to implement their religion into the university. Even schools that are represented by a religion are in danger of an ethnic group with other strong religious beliefs. With all of these problems with ethnic groups, could quickly multiply as they also act as networking groups to get more students who share the same background and beliefs to attend the school. Even graduates could carry this on to their future employers, although experts claim that no network group has ever set out to bring a company down, companies are the next things for college students and bring the risk of having their employees become far too separate from each other. The whole idea of diversity in a company is to make the work place and as uniformly supportive to all cultural backgrounds. By allowing these groups to form, the companies are moving further away from supposedly a fair and diverse population of workers. The segregation of ethnic groups from the rest of the student body largely contributes to a decline of ethnic relations on campus. If I were to see the other side of this and disagree, I would believe that if students of the same ethnic background only dined and participated in ethnic student activities, then it would not contribute to a decline of ethnic relations on campus. Instead, it would have students who tend to stay surrounded by people of the same ethnic background could feel more free, comfortable and accepted; thus bringing improvements leading to higher grades in the classroom and a more humble attitude towards life. Also being placed within a group will most likely encourage students to go out socialize, take part in campus activities, and enjoy campus life. This will lead to them interacting with people from different backgrounds. These ethnic groups can also play a vital role in campus activity by hosting fundraisers, parties, or sporting events. Another benefit to being part of an ethnic group is that many voices are better than one. So by having your voice heard out there will help put an end to any discrimination that was happening and will strengthen the campus’ ethnic relations. Future students looking to attend the school in the years to come may visit the campus and see a group of people who share the same ethnicity or interest as themselves and allure them to come to that university. These groups working as a form of networking could be a massive tool in attracting a diverse student body while at the same time strengthening ethnic relationships. Having been asked to support the statement or disagree, I would have to say that I agree with the statement. In order to be diverse, the student body as a whole it needs to be integrated at all times. It is one thing to live and be part of a group with people who share the same background; however, they should also be with people of other ethnicities and backgrounds. One group only interacting with themselves and not acknowledging other groups forms poor ethnic relations habits. I also feel that most ethnic groups will express a religion that will stir up controversy with other groups for one reason or another. By using the groups as a way to persuade new students to attend the school, the groups will quickly grow and possibly cause even more conflict to the university. Ethnic groups will bring nothing more than poor ethnic relations habits to campus and future graduates workplaces causing only a decline in ethnic relations.

Identifying Clusters in High Dimensional Data

Identifying Clusters in High Dimensional Data â€Å"Ask those who remember, are mindful if you do not know).† (Holy Quran, 6:43) Removal Of Redundant Dimensions To Find Clusters In N-Dimensional Data Using Subspace Clustering Abstract The data mining has emerged as a powerful tool to extract knowledge from huge databases. Researchers have introduced several machine learning algorithms to explore the databases to discover information, hidden patterns, and rules from the data which were not known at the data recording time. Due to the remarkable developments in the storage capacities, processing and powerful algorithmic tools, practitioners are developing new and improved algorithms and techniques in several areas of data mining to discover the rules and relationship among the attributes in simple and complex higher dimensional databases. Furthermore data mining has its implementation in large variety of areas ranging from banking to marketing, engineering to bioinformatics and from investment to risk analysis and fraud detection. Practitioners are analyzing and implementing the techniques of artificial neural networks for classification and regression problems because of accuracy, efficiency. The aim of his short r esearch project is to develop a way of identifying the clusters in high dimensional data as well as redundant dimensions which can create a noise in identifying the clusters in high dimensional data. Techniques used in this project utilizes the strength of the projections of the data points along the dimensions to identify the intensity of projection along each dimension in order to find cluster and redundant dimension in high dimensional data. 1 Introduction In numerous scientific settings, engineering processes, and business applications ranging from experimental sensor data and process control data to telecommunication traffic observation and financial transaction monitoring, huge amounts of high-dimensional measurement data are produced and stored. Whereas sensor equipments as well as big storage devices are getting cheaper day by day, data analysis tools and techniques wrap behind. Clustering methods are common solutions to unsupervised learning problems where neither any expert knowledge nor some helpful annotation for the data is available. In general, clustering groups the data objects in a way that similar objects get together in clusters whereas objects from different clusters are of high dissimilarity. However it is observed that clustering disclose almost no structure even it is known there must be groups of similar objects. In many cases, the reason is that the cluster structure is stimulated by some subsets of the spaces dim ensions only, and the many additional dimensions contribute nothing other than making noise in the data that hinder the discovery of the clusters within that data. As a solution to this problem, clustering algorithms are applied to the relevant subspaces only. Immediately, the new question is how to determine the relevant subspaces among the dimensions of the full space. Being faced with the power set of the set of dimensions a brute force trial of all subsets is infeasible due to their exponential number with respect to the original dimensionality. In high dimensional data, as dimensions are increasing, the visualization and representation of the data becomes more difficult and sometimes increase in the dimensions can create a bottleneck. More dimensions mean more visualization or representation problems in the data. As the dimensions are increased, the data within those dimensions seems dispersing towards the corners / dimensions. Subspace clustering solves this problem by identifying both problems in parallel. It solves the problem of relevant subspaces which can be marked as redundant in high dimensional data. It also solves the problem of finding the cluster structures within that dataset which become apparent in these subspaces. Subspace clustering is an extension to the traditional clustering which automatically finds the clusters present in the subspace of high dimensional data space that allows better clustering the data points than the original space and it works even when the curse of dimensionality occurs. The most o f the clustering algorithms have been designed to discover clusters in full dimensional space so they are not effective in identifying the clusters that exists within subspace of the original data space. The most of the clustering algorithms produces clustering results based on the order in which the input records were processed [2]. Subspace clustering can identify the different cluster within subspaces which exists in the huge amount of sales data and through it we can find which of the different attributes are related. This can be useful in promoting the sales and in planning the inventory levels of different products. It can be used for finding the subspace clusters in spatial databases and some useful decisions can be taken based on the subspace clusters identified [2]. The technique used here for indentifying the redundant dimensions which are creating noise in the data in order to identifying the clusters consist of drawing or plotting the data points in all dimensions. At second step the projection of all data points along each dimension are plotted. At the third step the unions of projections along each dimension are plotted using all possible combinations among all no. of dimensions and finally the union of all projection along all dimensions and analyzed, it will show the contribution of each dimension in indentifying the cluster which will be represented by the weight of projection. If any of the given dimension is contributing very less in order to building the weight of projection, that dimension can be considered as redundant, which means this dimension is not so important to identify the clusters in given data. The details of this strategy will be covered in later chapters. 2 Data Mining 2.1 What is Data Mining? Data mining is the process of analyzing data from different perspective and summarizing it for getting useful information. The information can be used for many useful purposes like increasing revenue, cuts costs etc. The data mining process also finds the hidden knowledge and relationship within the data which was not known while data recording. Describing the data is the first step in data mining, followed by summarizing its attributes (like standard deviation mean etc). After that data is reviewed using visual tools like charts and graphs and then meaningful relations are determined. In the data mining process, the steps of collecting, exploring and selecting the right data are critically important. User can analyze data from different dimensions categorize and summarize it. Data mining finds the correlation or patterns amongst the fields in large databases. Data mining has a great potential to help companies to focus on their important information in their data warehouse. It can predict the future trends and behaviors and allows the business to make more proactive and knowledge driven decisions. It can answer the business questions that were traditionally much time consuming to resolve. It scours databases for hidden patterns for finding predictive information that experts may miss it might lies beyond their expectations. Data mining is normally used to transform the data into information or knowledge. It is commonly used in wide range of profiting practices such as marketing, fraud detection and scientific discovery. Many companies already collect and refine their data. Data mining techniques can be implemented on existing platforms for enhance the value of information resources. Data mining tools can analyze massive databases to deliver answers to the questions. Some other terms contains similar meaning from data mining such as â€Å"Knowledge mining† or â€Å"Knowledge Extraction† or â€Å"Pattern Analysis†. Data mining can also be treated as a Knowledge Discovery from Data (KDD). Some people simply mean the data mining as an essential step in Knowledge discovery from a large data. The process of knowledge discovery from data contains following steps. * Data cleaning (removing the noise and inconsistent data) * Data Integration (combining multiple data sources) * Data selection (retrieving the data relevant to analysis task from database) * Data Transformation (transforming the data into appropriate forms for mining by performing summary or aggregation operations) * Data mining (applying the intelligent methods in order to extract data patterns) * Pattern evaluation (identifying the truly interesting patterns representing knowledge based on some measures) * Knowledge representation (representing knowledge techniques that are used to present the mined knowledge to the user) 2.2 Data Data can be any type of facts, or text, or image or number which can be processed by computer. Todays organizations are accumulating large and growing amounts of data in different formats and in different databases. It can include operational or transactional data which includes costs, sales, inventory, payroll and accounting. It can also include nonoperational data such as industry sales and forecast data. It can also include the meta data which is, data about the data itself, such as logical database design and data dictionary definitions. 2.3 Information The information can be retrieved from the data via patterns, associations or relationship may exist in the data. For example the retail point of sale transaction data can be analyzed to yield information about the products which are being sold and when. 2.4 Knowledge Knowledge can be retrieved from information via historical patterns and the future trends. For example the analysis on retail supermarket sales data in promotional efforts point of view can provide the knowledge buying behavior of customer. Hence items which are at most risk for promotional efforts can be determined by manufacturer easily. 2.5 Data warehouse The advancement in data capture, processing power, data transmission and storage technologies are enabling the industry to integrate their various databases into data warehouse. The process of centralizing and retrieving the data is called data warehousing. Data warehousing is new term but concept is a bit old. Data warehouse is storage of massive amount of data in electronic form. Data warehousing is used to represent an ideal way of maintaining a central repository for all organizational data. Purpose of data warehouse is to maximize the user access and analysis. The data from different data sources are extracted, transformed and then loaded into data warehouse. Users / clients can generate different types of reports and can do business analysis by accessing the data warehouse. Data mining is primarily used today by companies with a strong consumer focus retail, financial, communication, and marketing organizations. It allows these organizations to evaluate associations between certain internal external factors. The product positioning, price or staff skills can be example of internal factors. The external factor examples can be economic indicators, customer demographics and competition. It also allows them to calculate the impact on sales, corporate profits and customer satisfaction. Furthermore it allows them to summarize the information to look detailed transactional data. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by its capabilities. Data mining usually automates the procedure of searching predictive information in huge databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data very quickly. The targeted marketing can be an example of predictive problem. Data mining utilizes data on previous promotional mailings in order to recognize the targets most probably to increase return on investment as maximum as possible in future mailings. Tools used in data mining traverses through huge databases and discover previously unseen patterns in single step. Analysis on retail sales data to recognize apparently unrelated products which are usually purchased together can be an example of it. The more pattern discovery problems can include identifying fraudulent credit card transactions and identifying irregular data that could symbolize data entry input errors. When data mining tools are used on parallel processing systems of high performance, they are able to analy ze huge databases in very less amount of time. Faster or quick processing means that users can automatically experience with more details to recognize the complex data. High speed and quick response makes it actually possible for users to examine huge amounts of data. Huge databases, in turn, give improved and better predictions. 2.6 Descriptive and Predictive Data Mining Descriptive data mining aims to find patterns in the data that provide some information about what the data contains. It describes patterns in existing data, and is generally used to create meaningful subgroups such as demographic clusters. For example descriptions are in the form of Summaries and visualization, Clustering and Link Analysis. Predictive Data Mining is used to forecast explicit values, based on patterns determined from known results. For example, in the database having records of clients who have already answered to a specific offer, a model can be made that predicts which prospects are most probable to answer to the same offer. It is usually applied to recognize data mining projects with the goal to identify a statistical or neural network model or set of models that can be used to predict some response of interest. For example, a credit card company may want to engage in predictive data mining, to derive a (trained) model or set of models that can quickly identify tr ansactions which have a high probability of being fraudulent. Other types of data mining projects may be more exploratory in nature (e.g. to determine the cluster or divisions of customers), in which case drill-down descriptive and tentative methods need to be applied. Predictive data mining is goad oriented. It can be decomposed into following major tasks. * Data Preparation * Data Reduction * Data Modeling and Prediction * Case and Solution Analysis 2.7 Text Mining The Text Mining is sometimes also called Text Data Mining which is more or less equal to Text Analytics. Text mining is the process of extracting/deriving high quality information from the text. High quality information is typically derived from deriving the patterns and trends through means such as statistical pattern learning. It usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. The High Quality in text mining usually refers to some combination of relevance, novelty, and interestingness. The text categorization, concept/entity extraction, text clustering, sentiment analysis, production of rough taxonomies, entity relation modeling, document summarization can be included as text mining tasks. Text Mining is also known as the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. Linking together of the extracted information is the key element to create new facts or new hypotheses to be examined further by more conventional ways of experimentation. In text mining, the goal is to discover unknown information, something that no one yet knows and so could not have yet written down. The difference between ordinary data mining and text mining is that, in text mining the patterns are retrieved from natural language text instead of from structured databases of facts. Databases are designed and developed for programs to execute automatically; text is written for people to read. Most of the researchers think that it will need a full fledge simulation of how the brain works before that programs that read the way people do could be written. 2.8 Web Mining Web Mining is the technique which is used to extract and discover the information from web documents and services automatically. The interest of various research communities, tremendous growth of information resources on Web and recent interest in e-commerce has made this area of research very huge. Web mining can be usually decomposed into subtasks. * Resource finding: fetching intended web documents. * Information selection and pre-processing: selecting and preprocessing specific information from fetched web resources automatically. * Generalization: automatically discovers general patterns at individual and across multiple website * Analysis: validation and explanation of mined patterns. Web Mining can be mainly categorized into three areas of interest based on which part of Web needs to be mined: Web Content Mining, Web Structure Mining and Web Usage Mining. Web Contents Mining describes the discovery of useful information from the web contents, data and documents [10]. In past the internet consisted of only different types of services and data resources. But today most of the data is available over the internet; even digital libraries are also available on Web. The web contents consist of several types of data including text, image, audio, video, metadata as well as hyperlinks. Most of the companies are trying to transform their business and services into electronic form and putting it on Web. As a result, the databases of the companies which were previously residing on legacy systems are now accessible over the Web. Thus the employees, business partners and even end clients are able to access the companys databases over the Web. Users are accessing the application s over the web via their web interfaces due to which the most of the companies are trying to transform their business over the web, because internet is capable of making connection to any other computer anywhere in the world [11]. Some of the web contents are hidden and hence cannot be indexed. The dynamically generated data from the results of queries residing in the database or private data can fall in this area. Unstructured data such as free text or semi structured data such as HTML and fully structured data such as data in the tables or database generated web pages can be considered in this category. However unstructured text is mostly found in the web contents. The work on Web content mining is mostly done from 2 point of views, one is IR and other is DB point of view. â€Å"From IR view, web content mining assists and improves the information finding or filtering to the user. From DB view web content mining models the data on the web and integrates them so that the more soph isticated queries other than keywords could be performed. [10]. In Web Structure Mining, we are more concerned with the structure of hyperlinks within the web itself which can be called as inter document structure [10]. It is closely related to the web usage mining [14]. Pattern detection and graphs mining are essentially related to the web structure mining. Link analysis technique can be used to determine the patterns in the graph. The search engines like Google usually uses the web structure mining. For example, the links are mined and one can then determine the web pages that point to a particular web page. When a string is searched, a webpage having most number of links pointed to it may become first in the list. Thats why web pages are listed based on rank which is calculated by the rank of web pages pointed to it [14]. Based on web structural data, web structure mining can be divided into two categories. The first kind of web structure mining interacts with extracting patterns from the hyperlinks in the web. A hyperlink is a structural comp onent that links or connects the web page to a different web page or different location. The other kind of the web structure mining interacts with the document structure, which is using the tree-like structure to analyze and describe the HTML or XML tags within the web pages. With continuous growth of e-commerce, web services and web applications, the volume of clickstream and user data collected by web based organizations in their daily operations has increased. The organizations can analyze such data to determine the life time value of clients, design cross marketing strategies etc. [13]. The Web usage mining interacts with data generated by users clickstream. â€Å"The web usage data includes web server access logs, proxy server logs, browser logs, user profile, registration data, user sessions, transactions, cookies, user queries, bookmark data, mouse clicks and scrolls and any other data as a result of interaction† [10]. So the web usage mining is the most important task of the web mining [12]. Weblog databases can provide rich information about the web dynamics. In web usage mining, web log records are mined to discover the user access patterns through which the potential customers can be identified, quality of internet services can be enhanc ed and web server performance can be improved. Many techniques can be developed for implementation of web usage mining but it is important to know that success of such applications depends upon what and how much valid and reliable knowledge can be discovered the log data. Most often, the web logs are cleaned, condensed and transformed before extraction of any useful and significant information from weblog. Web mining can be performed on web log records to find associations patterns, sequential patterns and trend of web accessing. The overall Web usage mining process can be divided into three inter-dependent stages: data collection and pre-processing, pattern discovery, and pattern analysis [13]. In the data collection preprocessing stage, the raw data is collected, cleaned and transformed into a set of user transactions which represents the activities of each user during visits to the web site. In the pattern discovery stage, statistical, database, and machine learning operations a re performed to retrieve hidden patterns representing the typical behavior of users, as well as summary of statistics on Web resources, sessions, and users. 3 Classification 3.1 What is Classification? As the quantity and the variety increases in the available data, it needs some robust, efficient and versatile data categorization technique for exploration [16]. Classification is a method of categorizing class labels to patterns. It is actually a data mining methodology used to predict group membership for data instances. For example, one may want to use classification to guess whether the weather on a specific day would be â€Å"sunny†, â€Å"cloudy† or â€Å"rainy†. The data mining techniques which are used to differentiate similar kind of data objects / points from other are called clustering. It actually uses attribute values found in the data of one class to distinguish it from other types or classes. The data classification majorly concerns with the treatment of the large datasets. In classification we build a model by analyzing the existing data, describing the characteristics of various classes of data. We can use this model to predict the class/type of new data. Classification is a supervised machine learning procedure in which individual items are placed in a group based on quantitative information on one or more characteristics in the items. Decision Trees and Bayesian Networks are the examples of classification methods. One type of classification is Clustering. This is process of finding the similar data objects / points within the given dataset. This similarity can be in the meaning of distance measures or on any other parameter, depending upon the need and the given data. Classification is an ancient term as well as a modern one since classification of animals, plants and other physical objects is still valid today. Classification is a way of thinking about things rather than a study of things itself so it draws its theory and application from complete range of human experiences and thoughts [18]. From a bigger picture, classification can include medical patients based on disease, a set of images containing red rose from an image database, a set of documents describing â€Å"classification† from a document/text database, equipment malfunction based on cause and loan applicants based on their likelihood of payment etc. For example in later case, the problem is to predict a new applicants loans eligibility given old data about customers. There are many techniques which are used for data categorization / classification. The most common are Decision tree classifier and Bayesian classifiers. 3.2 Types of Classification There are two types of classification. One is supervised classification and other is unsupervised classification. Supervised learning is a machine learning technique for discovering a function from training data. The training data contains the pairs of input objects, and their desired outputs. The output of the function can be a continuous value which can be called regression, or can predict a class label of the input object which can be called as classification. The task of the supervised learner is to predict the value of the function for any valid input object after having seen a number of training examples (i.e. pairs of input and target output). To achieve this goal, the learner needs to simplify from the presented data to hidden situations in a meaningful way. The unsupervised learning is a class of problems in machine learning in which it is needed to seek to determine how the data are organized. It is distinguished from supervised learning in that the learner is given only unknown examples. Unsupervised learning is nearly related to the problem of density estimation in statistics. However unsupervised learning also covers many other techniques that are used to summarize and explain key features of the data. One form of unsupervised learning is clustering which will be covered in next chapter. Blind source partition based on Independent Component Analysis is another example. Neural network models, adaptive resonance theory and the self organizing maps are most commonly used unsupervised learning algorithms. There are many techniques for the implementation of supervised classification. We will be discussing two of them which are most commonly used which are Decision Trees classifiers and Naà ¯ve Bayesian Classifiers. 3.2.1 Decision Trees Classifier There are many alternatives to represent classifiers. The decision tree is probably the most widely used approach for this purpose. It is one of the most widely used supervised learning methods used for data exploration. It is easy to use and can be represented in if-then-else statements/rules and can work well in noisy data as well [16]. Tree like graph or decisions models and their possible consequences including resource costs, chance event, outcomes, and utilities are used in decision trees. Decision trees are most commonly used in specifically in decision analysis, operations research, to help in identifying a strategy most probably to reach a target. In machine learning and data mining, a decision trees are used as predictive model; means a planning from observations calculations about an item to the conclusions about its target value. More descriptive names for such tree models are classification tree or regression tree. In these tree structures, leaves are representing class ifications and branches are representing conjunctions of features those lead to classifications. The machine learning technique for inducing a decision tree from data is called decision tree learning, or decision trees. Decision trees are simple but powerful form of multiple variable analyses [15]. Classification is done by tree like structures that have different test criteria for a variable at each of the nodes. New leaves are generated based on the results of the tests at the nodes. Decision Tree is a supervised learning system in which classification rules are constructed from the decision tree. Decision trees are produced by algorithms which identify various ways splitting data set into branch like segment. Decision tree try to find out a strong relationship between input and target values within the dataset [15]. In tasks classification, decision trees normally visualize that what steps should be taken to reach on classification. Every decision tree starts with a parent node called root node which is considered to be the parent of every other node. Each node in the tree calculates an attribute in the data and decides which path it should follow. Typically the decision test is comparison of a value against some constant. Classification with the help of decision tree is done by traversing from the root node up to a leaf node. Decision trees are able to represent and classify the diverse types of data. The simplest form of data is numerical data which is most familiar too. Organizing nominal data is also required many times in many situations. Nominal quantities are normally represented via discrete set of symbols. For example weather condition can be described in either nominal fashion or numeric. Quantification can be done about temperature by saying that it is eleven degrees Celsius or fifty two degrees Fahrenheit. The cool, mild, cold, warm or hot terminologies can also be sued. The former is a type of numeric data while and the latter is an example of nominal data. More precisely, the example of cool, mild, cold, warm and hot is a special type of nominal data, expressed as ordinal data. Ordinal data usually has an implicit assumption of ordered relationships among the values. In the weather example, purely nominal description like rainy, overcast and sunny can also be added. These values have no relationships or distance measures among each other. Decision Trees are those types of trees where each node is a question, each branch is an answer to a question, and each leaf is a result. Here is an example of Decision tree. Roughly, the idea is based upon the number of stock items; we have to make different decisions. If we dont have much, you buy at any cost. If you have a lot of items then you only buy if it is inexpensive. Now if stock items are less than 10 then buy all if unit price is less than 10 otherwise buy only 10 items. Now if we have 10 to 40 items in the stock then check unit price. If unit price is less than 5 £ then buy only 5 items otherwise no need to buy anything expensive since stock is good already. Now if we have more than 40 items in the stock, then buy 5 if and only if price is less than 2 £ otherwise no need to buy too expensive items. So in this way decision trees help us to make a decision at each level. Here is another example of decision tree, representing the risk factor associated with the rash driving. The root node at the top of the tree structure is showing the feature that is split first for highest discrimination. The internal nodes are showing decision rules on one or more attributes while leaf nodes are class labels. A person having age less than 20 has very high risk while a person having age greater than 30 has a very low risk. A middle category; a person having age greater than 20 but less than 30 depend upon another attribute which is car type. If car type is of sports then there is again high risk involved while if family car is used then there is low risk involved. In the field of sciences engineering and in the applied areas including business intelligence and data mining, many useful features are being introduced as the result of evolution of decision trees. * With the help of transformation in decision trees, the volume of data can be reduced into more compact form that preserves the major characteristic Identifying Clusters in High Dimensional Data Identifying Clusters in High Dimensional Data â€Å"Ask those who remember, are mindful if you do not know).† (Holy Quran, 6:43) Removal Of Redundant Dimensions To Find Clusters In N-Dimensional Data Using Subspace Clustering Abstract The data mining has emerged as a powerful tool to extract knowledge from huge databases. Researchers have introduced several machine learning algorithms to explore the databases to discover information, hidden patterns, and rules from the data which were not known at the data recording time. Due to the remarkable developments in the storage capacities, processing and powerful algorithmic tools, practitioners are developing new and improved algorithms and techniques in several areas of data mining to discover the rules and relationship among the attributes in simple and complex higher dimensional databases. Furthermore data mining has its implementation in large variety of areas ranging from banking to marketing, engineering to bioinformatics and from investment to risk analysis and fraud detection. Practitioners are analyzing and implementing the techniques of artificial neural networks for classification and regression problems because of accuracy, efficiency. The aim of his short r esearch project is to develop a way of identifying the clusters in high dimensional data as well as redundant dimensions which can create a noise in identifying the clusters in high dimensional data. Techniques used in this project utilizes the strength of the projections of the data points along the dimensions to identify the intensity of projection along each dimension in order to find cluster and redundant dimension in high dimensional data. 1 Introduction In numerous scientific settings, engineering processes, and business applications ranging from experimental sensor data and process control data to telecommunication traffic observation and financial transaction monitoring, huge amounts of high-dimensional measurement data are produced and stored. Whereas sensor equipments as well as big storage devices are getting cheaper day by day, data analysis tools and techniques wrap behind. Clustering methods are common solutions to unsupervised learning problems where neither any expert knowledge nor some helpful annotation for the data is available. In general, clustering groups the data objects in a way that similar objects get together in clusters whereas objects from different clusters are of high dissimilarity. However it is observed that clustering disclose almost no structure even it is known there must be groups of similar objects. In many cases, the reason is that the cluster structure is stimulated by some subsets of the spaces dim ensions only, and the many additional dimensions contribute nothing other than making noise in the data that hinder the discovery of the clusters within that data. As a solution to this problem, clustering algorithms are applied to the relevant subspaces only. Immediately, the new question is how to determine the relevant subspaces among the dimensions of the full space. Being faced with the power set of the set of dimensions a brute force trial of all subsets is infeasible due to their exponential number with respect to the original dimensionality. In high dimensional data, as dimensions are increasing, the visualization and representation of the data becomes more difficult and sometimes increase in the dimensions can create a bottleneck. More dimensions mean more visualization or representation problems in the data. As the dimensions are increased, the data within those dimensions seems dispersing towards the corners / dimensions. Subspace clustering solves this problem by identifying both problems in parallel. It solves the problem of relevant subspaces which can be marked as redundant in high dimensional data. It also solves the problem of finding the cluster structures within that dataset which become apparent in these subspaces. Subspace clustering is an extension to the traditional clustering which automatically finds the clusters present in the subspace of high dimensional data space that allows better clustering the data points than the original space and it works even when the curse of dimensionality occurs. The most o f the clustering algorithms have been designed to discover clusters in full dimensional space so they are not effective in identifying the clusters that exists within subspace of the original data space. The most of the clustering algorithms produces clustering results based on the order in which the input records were processed [2]. Subspace clustering can identify the different cluster within subspaces which exists in the huge amount of sales data and through it we can find which of the different attributes are related. This can be useful in promoting the sales and in planning the inventory levels of different products. It can be used for finding the subspace clusters in spatial databases and some useful decisions can be taken based on the subspace clusters identified [2]. The technique used here for indentifying the redundant dimensions which are creating noise in the data in order to identifying the clusters consist of drawing or plotting the data points in all dimensions. At second step the projection of all data points along each dimension are plotted. At the third step the unions of projections along each dimension are plotted using all possible combinations among all no. of dimensions and finally the union of all projection along all dimensions and analyzed, it will show the contribution of each dimension in indentifying the cluster which will be represented by the weight of projection. If any of the given dimension is contributing very less in order to building the weight of projection, that dimension can be considered as redundant, which means this dimension is not so important to identify the clusters in given data. The details of this strategy will be covered in later chapters. 2 Data Mining 2.1 What is Data Mining? Data mining is the process of analyzing data from different perspective and summarizing it for getting useful information. The information can be used for many useful purposes like increasing revenue, cuts costs etc. The data mining process also finds the hidden knowledge and relationship within the data which was not known while data recording. Describing the data is the first step in data mining, followed by summarizing its attributes (like standard deviation mean etc). After that data is reviewed using visual tools like charts and graphs and then meaningful relations are determined. In the data mining process, the steps of collecting, exploring and selecting the right data are critically important. User can analyze data from different dimensions categorize and summarize it. Data mining finds the correlation or patterns amongst the fields in large databases. Data mining has a great potential to help companies to focus on their important information in their data warehouse. It can predict the future trends and behaviors and allows the business to make more proactive and knowledge driven decisions. It can answer the business questions that were traditionally much time consuming to resolve. It scours databases for hidden patterns for finding predictive information that experts may miss it might lies beyond their expectations. Data mining is normally used to transform the data into information or knowledge. It is commonly used in wide range of profiting practices such as marketing, fraud detection and scientific discovery. Many companies already collect and refine their data. Data mining techniques can be implemented on existing platforms for enhance the value of information resources. Data mining tools can analyze massive databases to deliver answers to the questions. Some other terms contains similar meaning from data mining such as â€Å"Knowledge mining† or â€Å"Knowledge Extraction† or â€Å"Pattern Analysis†. Data mining can also be treated as a Knowledge Discovery from Data (KDD). Some people simply mean the data mining as an essential step in Knowledge discovery from a large data. The process of knowledge discovery from data contains following steps. * Data cleaning (removing the noise and inconsistent data) * Data Integration (combining multiple data sources) * Data selection (retrieving the data relevant to analysis task from database) * Data Transformation (transforming the data into appropriate forms for mining by performing summary or aggregation operations) * Data mining (applying the intelligent methods in order to extract data patterns) * Pattern evaluation (identifying the truly interesting patterns representing knowledge based on some measures) * Knowledge representation (representing knowledge techniques that are used to present the mined knowledge to the user) 2.2 Data Data can be any type of facts, or text, or image or number which can be processed by computer. Todays organizations are accumulating large and growing amounts of data in different formats and in different databases. It can include operational or transactional data which includes costs, sales, inventory, payroll and accounting. It can also include nonoperational data such as industry sales and forecast data. It can also include the meta data which is, data about the data itself, such as logical database design and data dictionary definitions. 2.3 Information The information can be retrieved from the data via patterns, associations or relationship may exist in the data. For example the retail point of sale transaction data can be analyzed to yield information about the products which are being sold and when. 2.4 Knowledge Knowledge can be retrieved from information via historical patterns and the future trends. For example the analysis on retail supermarket sales data in promotional efforts point of view can provide the knowledge buying behavior of customer. Hence items which are at most risk for promotional efforts can be determined by manufacturer easily. 2.5 Data warehouse The advancement in data capture, processing power, data transmission and storage technologies are enabling the industry to integrate their various databases into data warehouse. The process of centralizing and retrieving the data is called data warehousing. Data warehousing is new term but concept is a bit old. Data warehouse is storage of massive amount of data in electronic form. Data warehousing is used to represent an ideal way of maintaining a central repository for all organizational data. Purpose of data warehouse is to maximize the user access and analysis. The data from different data sources are extracted, transformed and then loaded into data warehouse. Users / clients can generate different types of reports and can do business analysis by accessing the data warehouse. Data mining is primarily used today by companies with a strong consumer focus retail, financial, communication, and marketing organizations. It allows these organizations to evaluate associations between certain internal external factors. The product positioning, price or staff skills can be example of internal factors. The external factor examples can be economic indicators, customer demographics and competition. It also allows them to calculate the impact on sales, corporate profits and customer satisfaction. Furthermore it allows them to summarize the information to look detailed transactional data. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by its capabilities. Data mining usually automates the procedure of searching predictive information in huge databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data very quickly. The targeted marketing can be an example of predictive problem. Data mining utilizes data on previous promotional mailings in order to recognize the targets most probably to increase return on investment as maximum as possible in future mailings. Tools used in data mining traverses through huge databases and discover previously unseen patterns in single step. Analysis on retail sales data to recognize apparently unrelated products which are usually purchased together can be an example of it. The more pattern discovery problems can include identifying fraudulent credit card transactions and identifying irregular data that could symbolize data entry input errors. When data mining tools are used on parallel processing systems of high performance, they are able to analy ze huge databases in very less amount of time. Faster or quick processing means that users can automatically experience with more details to recognize the complex data. High speed and quick response makes it actually possible for users to examine huge amounts of data. Huge databases, in turn, give improved and better predictions. 2.6 Descriptive and Predictive Data Mining Descriptive data mining aims to find patterns in the data that provide some information about what the data contains. It describes patterns in existing data, and is generally used to create meaningful subgroups such as demographic clusters. For example descriptions are in the form of Summaries and visualization, Clustering and Link Analysis. Predictive Data Mining is used to forecast explicit values, based on patterns determined from known results. For example, in the database having records of clients who have already answered to a specific offer, a model can be made that predicts which prospects are most probable to answer to the same offer. It is usually applied to recognize data mining projects with the goal to identify a statistical or neural network model or set of models that can be used to predict some response of interest. For example, a credit card company may want to engage in predictive data mining, to derive a (trained) model or set of models that can quickly identify tr ansactions which have a high probability of being fraudulent. Other types of data mining projects may be more exploratory in nature (e.g. to determine the cluster or divisions of customers), in which case drill-down descriptive and tentative methods need to be applied. Predictive data mining is goad oriented. It can be decomposed into following major tasks. * Data Preparation * Data Reduction * Data Modeling and Prediction * Case and Solution Analysis 2.7 Text Mining The Text Mining is sometimes also called Text Data Mining which is more or less equal to Text Analytics. Text mining is the process of extracting/deriving high quality information from the text. High quality information is typically derived from deriving the patterns and trends through means such as statistical pattern learning. It usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. The High Quality in text mining usually refers to some combination of relevance, novelty, and interestingness. The text categorization, concept/entity extraction, text clustering, sentiment analysis, production of rough taxonomies, entity relation modeling, document summarization can be included as text mining tasks. Text Mining is also known as the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. Linking together of the extracted information is the key element to create new facts or new hypotheses to be examined further by more conventional ways of experimentation. In text mining, the goal is to discover unknown information, something that no one yet knows and so could not have yet written down. The difference between ordinary data mining and text mining is that, in text mining the patterns are retrieved from natural language text instead of from structured databases of facts. Databases are designed and developed for programs to execute automatically; text is written for people to read. Most of the researchers think that it will need a full fledge simulation of how the brain works before that programs that read the way people do could be written. 2.8 Web Mining Web Mining is the technique which is used to extract and discover the information from web documents and services automatically. The interest of various research communities, tremendous growth of information resources on Web and recent interest in e-commerce has made this area of research very huge. Web mining can be usually decomposed into subtasks. * Resource finding: fetching intended web documents. * Information selection and pre-processing: selecting and preprocessing specific information from fetched web resources automatically. * Generalization: automatically discovers general patterns at individual and across multiple website * Analysis: validation and explanation of mined patterns. Web Mining can be mainly categorized into three areas of interest based on which part of Web needs to be mined: Web Content Mining, Web Structure Mining and Web Usage Mining. Web Contents Mining describes the discovery of useful information from the web contents, data and documents [10]. In past the internet consisted of only different types of services and data resources. But today most of the data is available over the internet; even digital libraries are also available on Web. The web contents consist of several types of data including text, image, audio, video, metadata as well as hyperlinks. Most of the companies are trying to transform their business and services into electronic form and putting it on Web. As a result, the databases of the companies which were previously residing on legacy systems are now accessible over the Web. Thus the employees, business partners and even end clients are able to access the companys databases over the Web. Users are accessing the application s over the web via their web interfaces due to which the most of the companies are trying to transform their business over the web, because internet is capable of making connection to any other computer anywhere in the world [11]. Some of the web contents are hidden and hence cannot be indexed. The dynamically generated data from the results of queries residing in the database or private data can fall in this area. Unstructured data such as free text or semi structured data such as HTML and fully structured data such as data in the tables or database generated web pages can be considered in this category. However unstructured text is mostly found in the web contents. The work on Web content mining is mostly done from 2 point of views, one is IR and other is DB point of view. â€Å"From IR view, web content mining assists and improves the information finding or filtering to the user. From DB view web content mining models the data on the web and integrates them so that the more soph isticated queries other than keywords could be performed. [10]. In Web Structure Mining, we are more concerned with the structure of hyperlinks within the web itself which can be called as inter document structure [10]. It is closely related to the web usage mining [14]. Pattern detection and graphs mining are essentially related to the web structure mining. Link analysis technique can be used to determine the patterns in the graph. The search engines like Google usually uses the web structure mining. For example, the links are mined and one can then determine the web pages that point to a particular web page. When a string is searched, a webpage having most number of links pointed to it may become first in the list. Thats why web pages are listed based on rank which is calculated by the rank of web pages pointed to it [14]. Based on web structural data, web structure mining can be divided into two categories. The first kind of web structure mining interacts with extracting patterns from the hyperlinks in the web. A hyperlink is a structural comp onent that links or connects the web page to a different web page or different location. The other kind of the web structure mining interacts with the document structure, which is using the tree-like structure to analyze and describe the HTML or XML tags within the web pages. With continuous growth of e-commerce, web services and web applications, the volume of clickstream and user data collected by web based organizations in their daily operations has increased. The organizations can analyze such data to determine the life time value of clients, design cross marketing strategies etc. [13]. The Web usage mining interacts with data generated by users clickstream. â€Å"The web usage data includes web server access logs, proxy server logs, browser logs, user profile, registration data, user sessions, transactions, cookies, user queries, bookmark data, mouse clicks and scrolls and any other data as a result of interaction† [10]. So the web usage mining is the most important task of the web mining [12]. Weblog databases can provide rich information about the web dynamics. In web usage mining, web log records are mined to discover the user access patterns through which the potential customers can be identified, quality of internet services can be enhanc ed and web server performance can be improved. Many techniques can be developed for implementation of web usage mining but it is important to know that success of such applications depends upon what and how much valid and reliable knowledge can be discovered the log data. Most often, the web logs are cleaned, condensed and transformed before extraction of any useful and significant information from weblog. Web mining can be performed on web log records to find associations patterns, sequential patterns and trend of web accessing. The overall Web usage mining process can be divided into three inter-dependent stages: data collection and pre-processing, pattern discovery, and pattern analysis [13]. In the data collection preprocessing stage, the raw data is collected, cleaned and transformed into a set of user transactions which represents the activities of each user during visits to the web site. In the pattern discovery stage, statistical, database, and machine learning operations a re performed to retrieve hidden patterns representing the typical behavior of users, as well as summary of statistics on Web resources, sessions, and users. 3 Classification 3.1 What is Classification? As the quantity and the variety increases in the available data, it needs some robust, efficient and versatile data categorization technique for exploration [16]. Classification is a method of categorizing class labels to patterns. It is actually a data mining methodology used to predict group membership for data instances. For example, one may want to use classification to guess whether the weather on a specific day would be â€Å"sunny†, â€Å"cloudy† or â€Å"rainy†. The data mining techniques which are used to differentiate similar kind of data objects / points from other are called clustering. It actually uses attribute values found in the data of one class to distinguish it from other types or classes. The data classification majorly concerns with the treatment of the large datasets. In classification we build a model by analyzing the existing data, describing the characteristics of various classes of data. We can use this model to predict the class/type of new data. Classification is a supervised machine learning procedure in which individual items are placed in a group based on quantitative information on one or more characteristics in the items. Decision Trees and Bayesian Networks are the examples of classification methods. One type of classification is Clustering. This is process of finding the similar data objects / points within the given dataset. This similarity can be in the meaning of distance measures or on any other parameter, depending upon the need and the given data. Classification is an ancient term as well as a modern one since classification of animals, plants and other physical objects is still valid today. Classification is a way of thinking about things rather than a study of things itself so it draws its theory and application from complete range of human experiences and thoughts [18]. From a bigger picture, classification can include medical patients based on disease, a set of images containing red rose from an image database, a set of documents describing â€Å"classification† from a document/text database, equipment malfunction based on cause and loan applicants based on their likelihood of payment etc. For example in later case, the problem is to predict a new applicants loans eligibility given old data about customers. There are many techniques which are used for data categorization / classification. The most common are Decision tree classifier and Bayesian classifiers. 3.2 Types of Classification There are two types of classification. One is supervised classification and other is unsupervised classification. Supervised learning is a machine learning technique for discovering a function from training data. The training data contains the pairs of input objects, and their desired outputs. The output of the function can be a continuous value which can be called regression, or can predict a class label of the input object which can be called as classification. The task of the supervised learner is to predict the value of the function for any valid input object after having seen a number of training examples (i.e. pairs of input and target output). To achieve this goal, the learner needs to simplify from the presented data to hidden situations in a meaningful way. The unsupervised learning is a class of problems in machine learning in which it is needed to seek to determine how the data are organized. It is distinguished from supervised learning in that the learner is given only unknown examples. Unsupervised learning is nearly related to the problem of density estimation in statistics. However unsupervised learning also covers many other techniques that are used to summarize and explain key features of the data. One form of unsupervised learning is clustering which will be covered in next chapter. Blind source partition based on Independent Component Analysis is another example. Neural network models, adaptive resonance theory and the self organizing maps are most commonly used unsupervised learning algorithms. There are many techniques for the implementation of supervised classification. We will be discussing two of them which are most commonly used which are Decision Trees classifiers and Naà ¯ve Bayesian Classifiers. 3.2.1 Decision Trees Classifier There are many alternatives to represent classifiers. The decision tree is probably the most widely used approach for this purpose. It is one of the most widely used supervised learning methods used for data exploration. It is easy to use and can be represented in if-then-else statements/rules and can work well in noisy data as well [16]. Tree like graph or decisions models and their possible consequences including resource costs, chance event, outcomes, and utilities are used in decision trees. Decision trees are most commonly used in specifically in decision analysis, operations research, to help in identifying a strategy most probably to reach a target. In machine learning and data mining, a decision trees are used as predictive model; means a planning from observations calculations about an item to the conclusions about its target value. More descriptive names for such tree models are classification tree or regression tree. In these tree structures, leaves are representing class ifications and branches are representing conjunctions of features those lead to classifications. The machine learning technique for inducing a decision tree from data is called decision tree learning, or decision trees. Decision trees are simple but powerful form of multiple variable analyses [15]. Classification is done by tree like structures that have different test criteria for a variable at each of the nodes. New leaves are generated based on the results of the tests at the nodes. Decision Tree is a supervised learning system in which classification rules are constructed from the decision tree. Decision trees are produced by algorithms which identify various ways splitting data set into branch like segment. Decision tree try to find out a strong relationship between input and target values within the dataset [15]. In tasks classification, decision trees normally visualize that what steps should be taken to reach on classification. Every decision tree starts with a parent node called root node which is considered to be the parent of every other node. Each node in the tree calculates an attribute in the data and decides which path it should follow. Typically the decision test is comparison of a value against some constant. Classification with the help of decision tree is done by traversing from the root node up to a leaf node. Decision trees are able to represent and classify the diverse types of data. The simplest form of data is numerical data which is most familiar too. Organizing nominal data is also required many times in many situations. Nominal quantities are normally represented via discrete set of symbols. For example weather condition can be described in either nominal fashion or numeric. Quantification can be done about temperature by saying that it is eleven degrees Celsius or fifty two degrees Fahrenheit. The cool, mild, cold, warm or hot terminologies can also be sued. The former is a type of numeric data while and the latter is an example of nominal data. More precisely, the example of cool, mild, cold, warm and hot is a special type of nominal data, expressed as ordinal data. Ordinal data usually has an implicit assumption of ordered relationships among the values. In the weather example, purely nominal description like rainy, overcast and sunny can also be added. These values have no relationships or distance measures among each other. Decision Trees are those types of trees where each node is a question, each branch is an answer to a question, and each leaf is a result. Here is an example of Decision tree. Roughly, the idea is based upon the number of stock items; we have to make different decisions. If we dont have much, you buy at any cost. If you have a lot of items then you only buy if it is inexpensive. Now if stock items are less than 10 then buy all if unit price is less than 10 otherwise buy only 10 items. Now if we have 10 to 40 items in the stock then check unit price. If unit price is less than 5 £ then buy only 5 items otherwise no need to buy anything expensive since stock is good already. Now if we have more than 40 items in the stock, then buy 5 if and only if price is less than 2 £ otherwise no need to buy too expensive items. So in this way decision trees help us to make a decision at each level. Here is another example of decision tree, representing the risk factor associated with the rash driving. The root node at the top of the tree structure is showing the feature that is split first for highest discrimination. The internal nodes are showing decision rules on one or more attributes while leaf nodes are class labels. A person having age less than 20 has very high risk while a person having age greater than 30 has a very low risk. A middle category; a person having age greater than 20 but less than 30 depend upon another attribute which is car type. If car type is of sports then there is again high risk involved while if family car is used then there is low risk involved. In the field of sciences engineering and in the applied areas including business intelligence and data mining, many useful features are being introduced as the result of evolution of decision trees. * With the help of transformation in decision trees, the volume of data can be reduced into more compact form that preserves the major characteristic

The Alliance Between Renault Nissan Marketing Essay

The Alliance Between Renault Nissan Marketing Essay In the age of globalisation companies are trying to cope with consequences of this historical process. Scholars in this field have noticed that companies could either merge or conclude an alliance to cope with globalisation. The scholars, however, differ among themselves as to which is better, alliance or merger. By being global a firm would have a better chance to enter a new market, and increase both its global market share and global competitive advantage (Shenkar,2008,p.303,332). The two processes differ in terms of their meanings and the reasons for choosing one of them rather than the other. International mergers are when two firms from different countries, and have their own capabilities, agree to integrate in order to create a stronger core competency in the global market (Shenakr2008:303). However, strategic alliances are contracts between two parties from different countries, when they agree to cooperate in order to do a particular task(Charles, international Business, p.411).This shared task cant be realised and create a value unless the two parties work together(Andrew, Strategic alliances, P.404).(I can delete this phrase) In the case of Renault-Nissan, it is preferable to have an alliance than merger for many reasons. Charles Hill (int. business:412) claims that Alliances, would facilitate more than mergers the entrance for companies to new geographical phases where there are some restrictions on foreign investments. (Comprehensive cases,p. 312)the two companies had their own capabilities in their own market. Renault for instance, already existed in Europe and North America, and was well-known for its design and marketing. At the same time Nissan was the powerhouse engineering in Japan, Europe and North America. Therefore, there was a good chance for Renault to Enter the Japanese market where there are many barriers from the Japanese government. Synergy however, is vital for alliance. According to Shenkar( 2008: 333) alliance would be more rational when the two firms look for further synergy in their financial, technological aims. He adds, this alliance would provide the two parties with complementary resources and capabilities(HSenkar, 2008). This synergy between two companies was the key element for choosing Nissan-Renault alliance. According to Chosn, the manager of the alliance: we said from the beginning that we were not looking for a merger, but rather to get greater value from synergy between the two companies (Emerson, the interview). According to Chosn, the reason for choosing alliance rather then merger was that both companies were looking for turnaround. Although alliance was more risky than merger, yet they chose it because they thought it would give them more opportunities to develop.(Emerson, the interview). However, despite the advantages Nissan-Renault gained from the alliance, they faced challenges. One of the challenges is whether the alliance would lead to an increase or decrease in the price share (Ernst Halvey, When, p. 48).This was a real challenge for Nissan, whose share price fell when it entered the alliance.(Comprehensive cases). Furthermore, the two companies had a challenge of cross-culture problems. However, with their ability to focus on the work objective they were able to succeed. 508 Renault before and after alliance The alliance between Renault Nissan, as indicated by the results in March 2004, was an outstanding paradigm of a successful alliance around the world. However, before 1999, the prospective of forming an alliance between these two firms was not such rosy. From Renaults point of view, various factors were strengthening the former opinion. Firstly, (Morosini P. Dec 2004) Renault was recovering during 1996 and 1998-9 turning losses of US$680 million into combined profits of US$1.65 billion. Moreover, the failure to merge with Volvo in 1995 had left its mark on the company and any further attempts to a new alliance were confronted distrustfully. In addition, the fact that both firms were playing a dominant role in the auto industry of their countries was indicating that a potential alliance was going to collapse in a decision-making stalemate. Nevertheless, the supporters of the latter argument were gainsaid. The mutual benefits that they were going to absorb from the alliance laid aside the potential problems and both parties focused on the success of the alliance. This was a crucial challenge, which they managed to handle by learning to trust each other, be truthful and honest during the negotiations. Additionally, (Bartlett C., Ghoshal S., Beamish P., 2008) by forming joint study teams, in order to test their companies ability to work cooperatively, they minimized the cultural stereotypes and set the base for exploiting joint synergies. The two companies were so complementary in terms of geography, product ranges and personality that inevitably the future was foreboding promising. Besides, this process gave Renault an advantage over competitive suitors such as Ford and DaimlerChrysler, which focused only on finding synergies on past and current advantages rather than on a prospective productive future. On this basis, Renault, through the alliance with Nissan, achieved to gain international structure which enabled it to deal successfully with the changes which were taking place on the world automobile stage. Thereby, Global synergies and the expansion of its production to foreign , until then, markets like Japan, North America and Asia enhanced its potential and made it a countable member in the auto industry. 357 Nissan before and after alliance Nissans history starts from the early of 1933. Nissan is a Japanese automobile manufacturer which achieves, through the years to have strong market presence in Asia and US. Except for the fact that Nissan was a highly emblematic symbol of Japans industrial strength, had also a number of strong points such as technological and engineering competence, and also was good at making large cars. In late March 1999 Nissan and Renault sign an agreement for a Global Alliance. Aim for this agreement was to provide an advantage and achieve profitable growth in both companies. However, Nissan was nearly bankrupt and faced significant debt problem when the alliance formed. One of the major reasons for this debt and financial difficulty was the fact that Nissan invested a lot of money in different companies and this has a result, Nissan not be in position to invest money in the company and its products (Ghosn, 2002). Therefore the company for a long time did not have any profit and this made the debt for Nissan in 1999 to reach the US $22 billion. Furthermore, during the same year (1999), the domestic market share had fallen from 17.4% to 13%. Have in mind this and after that Daimler Chrysler and Ford refused the idea of a partnership and broken of the alliance talk with Nissan, the company resorted to the strategic alliance with Renault, where both companies had clear idea of what they wanted. The alliance was vital for the two companies as Nissan needed Renaults cash in order to reduce its debt problem and Renault wanted to learn from Nissans success in US and Asia which was essential for the expansion in its market. During the period of social initiation process, of six months, many advantages arose over competitors as they carried out static analytical evaluations and they focused on finding collaborations based on their past and current strengths rather than on jointly future. In order to accomplish this, Nissan had change significantly to redeem its profitability and competiveness. First Nissan quit the investments in other companies, in other words the keiretsu which is a Japanese traditional rule that requires all the companies in Japan to have long-term purchasing relationship, intense collaboration and frequent exchange of personnel and technology between companies and selected suppliers (Okamura, 2005). The personal management also had changed and whereas Nissan in the past appraised their employees based on the period that they were working for the company, now they changed the criteria of evaluation by looking on the performance of each employee. Further they set up a common language i.e. English and they have created nine Cross-functional teams. By the implementation of the above changes, Nissan manage to cut down in purchasing cost, to reduce suppliers, to close overlapping outlets and plants and finally to reduce the work force. Through the alliance of Nissan and Renault, the benefits that arose were obvious and determinant. Transparent benchmarking allows two culturally diverse companies to share best practises and also the common platform and shared purchasing strategy had delivered huge cost of savings. Noticeable is the fact that in order to preserver corporate identities they decide to remain as separate managements, separate brands and separate companies while every decision was affecting both brands. The operation recommendation which arise from this alliance case provide valuable elements on how two companies, that are in the same situation like Renault and Nissan which show strength in different competence and regions of the world (Nissan had strong presence in Asia and US while Renault had presence in Europe), can approach the growing and competitive auto manufacturing global market. Therefore the success of this alliance is also interrelated with the synergy among the two companies and the framework of equality help the transfer of knowledge between foreign engineering teams. Finally Nissan successfully achieve to jump from seventh most valuable automobile company in the world to the fourth. 656 Structure of Alliances The aim of this section is to study structure of the alliances between Renault Nissan and advice about the best possible structure in futures alliances. Strategic alliances are said to be a source of competitive advantage. However there is a growing concern over their failure rates. One of the major causes is the inability to implement the appropriate governance structure and management control systems in the newly formed association (Smith, 2008). According to the study most of the companies form an alliance management team which manage across the organisation using Cross-Company Teams, Cross functional teams, Steering Committees and Alliance Board. By observation, Renaults was interested in creating respect between two alliance partners and respectively followed an Andean civilization approach to work together for six months before forming an alliance. (Donnelly, Morris Donnelly, 2005). The social initiation process provided Renault Nissan an advantage over its competitors such as Daimler Chrysler. The later company did not experiment social collaboration to develop the ability of sharing knowledge and building trust (Deresky, 2008). Therefore the structure in Renault Nissan was the result of, what the companies experienced during the social initiation stage. They formed a new board having 5 members each from the host companies. Further to speed the integration and improve communication process they created Nine Cross-functional teams (CFT) and 11 Cross-company teams (CCT) (Donnelly, Morris Donnelly, 2005: 434). More importantly, these teams had a Chair person from Renault, Vice Chair person from Nissan or vice-versa. Moreo ver the CFT was limited to 10 members from different departments such as purchasing, manufacturing which ensured progress between these departments (Donnelly, Morris Donnelly, 2005). As a result the alliance was able to launch 22 new car models in the next three years and increase the manufacturing capacity in Japan. Moreover the CCT created efficient synergies. One of the examples of amalgamation process was in Mexico. Renault had left the market in 1986 and Nissan was facing overcapacity in1999. So alliance decided to put the managers from both the companies together and recognise synergy opportunity. In just five months Renault cars were being manufactured out of Nissan plants and the capacity utilisation of the plant increased from 56% to nearly 100%. In summary cross-company teams allowed Renault- Nissan to first go through a social initiation experience and then move into a formal framework of collaboration and knowledge exchange (Deresky, 2008: 318). Similarly cross functional teams enhanced the process of integration. Cooperative Operation In this part of report, we will discuss how close collaboration between two companies in operational level has resulted in synergy. The main sources of data in this part are Renault and Nissan official websites. Supply chain management is one of the areas of key concern for global car manufacturers (John Gattorna). Major players in Car Industry are looking for revolutionary methods of management of their suppliers. In Renault-Nissan case, RNPO or Renault Nissan Purchasing Organization is a unique joint organization responsible for integrating purchasing Strategy. As we will describe in next paragraph, as a result of mutual engineering efforts, Renault and Nissan cars can share components. This fact allows the alliance to combine their purchasing orders. Therefore, not only the cost of order has reduced but RNPO defines worldwide purchasing strategy and now it is accountable for full purchase of Nissan and Renault. (www.renault.com) Another area for mutual cooperation between two companies is engineering which could be a lesson for other car manufacturers to reach economic of scale and scope. The key difference in Renault-Nissan case is concentrating on designing and producing components of car jointly instead of developing whole car from scratch. The alliance achieves economic of scale by producing in larger scales and economic of scope by manufacturing components which are compatible for different models of both brands. Moreover, one of top priorities of MNC is to find a way to reduce RD cost as well invest in new technologies with lower cost. For instance, according to Renault website, the alliance helps two companies to invest in advance technology like hybrid vehicles. In conclusion firms should successfully integrate their complimentary competencies to standardize their purchase orders and components manufacturing. Therefore they can reduce their cost and achieve greater outcomes. The role of Corporate and National Culture Corporate culture is the combined beliefs, values, ethics, procedures, and atmosphere of an organization ().One of the important issues raised in the Nissan Renault alliance is the management of two different cultures. While Renault strategy was liked western strategic orientation and Nissan was under the influence of corporate and national culture (Culpan.R,2002). Accordingly, the collective share of ideas and strategic management were effective and the employees of both companies could understand each other culture background, subsequently respect the identities of their colleagues as well as their values. Thus, Ghosn put cross- culture training programs for over 1500 employees from Renault to learn about the Japanese culture and 400 Nissan employees study the French culture (pooley, 2005).it was a first positive step in terms of creating a successful alliance of two different cultures. After presenting the French and Japanese culture, it was significant to understand their differ ences and how certain Hofstedes cultural theories (clenc, 2000) applicable to the case of Nissan and Renault. Japanese societies are well-known to be more collectivist and in opposite, French societies are based on individualistic efforts from employees. As the decision making process in Nissan was working the percept of groupthink, mostly the people who thought alike. Moreover, Nissan had a problem in terms of excess capacity that was based on an unofficial contract that existed between Japanese auto companies and their employees. Ghosn closed five factories and cutting some 21,000 jobs to broke this custom. He also took on the close network of relationships between auto companies and their suppliers, relationships denoted by a specific Japanese word, keiretsu (). An also after this situation as he employed new engineers in to the Nissan organisation, he decided to put English as formal language for company to deal with diversity of language spoken. In addition, in the Japanese cul ture, is not possible for a young employee to be manager for a colleague who is older in terms of age and seniority. However, the ECOs new system of promotion to begin restructuring the management process in company, was based on performance and efficiency, not employees age. As a result, the Renault-Nissan alliance has been hugely successful. There is broad acknowledgement by many at senior levels inside both companies that much credit for this must be given to their conscious effort to build cross-cultural understanding from the start. 396 Recommendations The success of alliance between Renault and Nissan proves that alliances can be a successful approach to expand globally. Therefore, we believe as a group that there are recommendations which could be taken into consideration, by any other car companies thinking to form a successful alliance to enter new markets. When two firms come to a decision of entering an alliance they should be aware of variations in cultures, languages and mentalities. This can be realised by understanding these differences and focussing more on the shared goals and objectives. In terms of operations, Renault Nissan can be used by other car companies in different ways. In supply chain management a similar organisation like RNPO can be established by other alliances to unify their purchasing orders and therefore reducing the cost of orders. In engineering we suggest for future alliances to focus on producing car components jointly instead of designing new cars entirely which failed in similar cases. By implementing the above strategies, the costs of RD can be reduced and higher output can be achieved by using shared resources. It is obvious from the case effective cross-culture management was one of the important key successes for the company. Thus, when two different companies spouse to work with each other in especial strategic alliance, creating a situation for managers and employees to learn about each other culture could be significant. Another step for developing corporate culture in this kind of alliances is making one formal language for employees to deal with diversity of spoken language. Moreover, determining shared values, knowledge and individual needs is important to create opportunities for future alliances. This can be achieved by following a social program similar to social initiation process of Renault-Nissan. Zaara Culpan. R, (2002) Global business alliances: theory and practice, Greenwood Publishing Group: United State America Pooley. J, (2005) The model alliance of Renault and Nissan: How to work successfully with overseas partners, Emerald Group Publishing Limited: Emerald Group Publishing Limited http://www.emeraldinsight.com/Insight/viewContentItem.do?contentType=ArticlehdAction=lnkpdfcontentId=1463638 Reference: Anu Kale, P., Dyer, J. Singh, H. (2001) Value Creation and Success in Strategic Alliances: Alliancing Skills and the Role of Alliance Structure and Systems. European Management Journal [Online], 19(5): 463 471. Smith, K. (2008) The relations between transactional characteristics, trust and risk in the start-up phase of a collaborative alliance. Management Accounting Research [Online], 19: 344 364. Deresky, H. (2007) International Management: Managing Across Borders and Cultures (6th Edition). New Jersey.Prentice Hall. pp. 312 319 Reference sophia Okamura, A. (2005). Beyond the Keiretsu. Article retrieved on November 2nd, 2006, from http://www.utofieldguide.com/articles/article_print1.cfm Ghosn, C. (2002). Saving the Business Without Losing the Company. Harvard Business Review. References marios: Bartlett C., Ghoshal S., Bearmish P., (2008), Transnational Management: Text, cases, and readings in Cross-Border Management, Fifth edition, New York, McGraw-Hill International Edition. Douin G., (5th April, 2002), Behind the scenes of the Renault-Nissan alliance, l Ecole de Paris, p.1-10. Morosini P., (Dec, 2004), Nurturing Successful Alliances Across Boundaries: Lessons from the Renault Nissan Case.

The Band Creed Essay -- Music Christianity Bands Papers

What If The band that comes to my mind when someone mentions abstract lyrics is the modern, popular, yet sophisticated group, Creed. I thoroughly enjoy their music, although not strictly for entertainment purposes. They have extremely creative ways of stating feelings, telling stories, and sharing ideas through the lyrics they produce. Their song, entitled "What If," is an excellent version of poetic writing that exemplifies mixed emotion and portrays an undefined depth in meaning. It is quite possible to interpret this particular song differently according to the listener's point of view and the personal background experiences that may influence their portrayal of the music. However, I see certain parallels to the Bible story of Jesus praying to God on the Mount of Olives. There are several lines that suggest the connotation of how fearful, anguished, and lonely Jesus felt before he was crucified. The very first verse is definitely the context of Jesus' pleading words of prayer to God shortly before he was betrayed and arrested. He was certainly in a state of depression, acutely aware of the fleeting minutes of freedom left for him on earth. The intense dread Jesus felt must have been overwhelming to him as he obediently kneeled in submission to God's will, desperately trying to understand His reasoning. At this particular time in the pre-crucifixion, it is only logical to associate the tremendous burden Jesus was going through with his great frustration of human ignorance and sin. Thus, the meaning of "not finding rhyme in reason, losing sense of time and seasons, and feeling beaten down, by men with no grounds," is explained to clarify the starting point of my interpretation. In the second set of phrases, the first two... ...e words of men who have no grounds I can't sleep beneath the trees of wisdom When your ax has cut the roots that feed them Forked tongues in bitter mouths Can drive a man to bleed from inside out What if you did? What if you lied? What if I avenge? What if eye for an eye? I've seen the wicked fruit of your vine Destroy the man who lacks a strong mind Human pride sings a vengeful song Inspired by the times you've been walked on My stage is shared by many millions Who lift their hands up high because they feel this We are one We are strong The more you hold us down the more we press on What if you did? What if you lied? What if I avenge? What if eye for an eye? I know I can't hold the hate inside my mind 'Cause what consumes your thoughts controls your life So I'll just ask a question What if? What if your words could be judged like a crime?

charles Kuralt :: essays research papers

10-Ninety Degrees North-   Ã‚  Ã‚  Ã‚  Ã‚     Ã‚  Ã‚  Ã‚  Ã‚  In this Chapter Kuralt is asked by one of his bosses to follow along with a man by the name of Ralph Plaisted and many of his friends. Kuralt was asked to make a documentary on the polar expedition that these men were taking part in. Kuralt’s job as the reported was to stay in a little weather shack and take a plane back forth between the shack and the explorers.   Ã‚  Ã‚  Ã‚  Ã‚  As they closer and closer to the North Pole the men we getting tired but something in Plaisted made every man want to keep going. On there trip the men had to overcome wind speeds up to 60 mph and cracks in the ice up to 4 ft wide. Then one day in may of 1967 the wind and cracked ice was just to much to overcome and the men had to turn back, Although the next year with careful planning and no fear Plaisted took off on this expedition again. As Kuralt stayed back in Cedar Rapids, IA over the radio to Plaisted he asked. â€Å" Where is you location?† and Plaisted reported back, â€Å" Ninety degrees north!†   Ã‚  Ã‚  Ã‚  Ã‚  I believe that that the moral of the story is that nothing in this world is impossible anything can happen at any given time. Like he said in this chapter how could people be starving in the richest nation in the world. And every one doubted Plaisted but look what happen he proved every body wrong. When you put you mind to it anything is possible. 11- Boxes on Wheels   Ã‚  Ã‚  Ã‚  Ã‚  This chapter began with Kuralt asking for a vacation and ended in him getting what he would be doing for the rest of his career. A box on wheels is what they call a mobile home. Kuralt and 3 other employees would travel around the nation in a mobile home searching for interesting stories to tell, but what might have been the most interesting was the mobile home.   Ã‚  Ã‚  Ã‚  Ã‚  The mobile home was always breaking down they couldn’t go a week with out something on the Cortez breaking down. Whether it was the carburetor, engine or the tires it broke atleast once. The crew went threw about 5 different mobile homes, none of which did the job. The worst of the worst was one day in the winter while driving through Utah in the middle of a blizzard the mobile home broke down.

Management Information System Essay

Question 1 : In the 21st century, information technology has emerged as the fundamental technology of business. Explain how it has helped business and state examples of real time situation which you have read about of experienced. Decision Support, Problem Analysis and Overall Control Business managers often need to make decisions that can affect the business’ fortunes one way or other. For example, a company with sales outlets or distributors spread over a wide geographic area might want to optimize the logistical operations of delivering merchandise to the outlets. The best solution might be affected by numerous factors such as demand patterns, availability of merchandise, distances involved and the option of using external carriers (who can find two way loads and might prove a lesser cost option over long distances) instead of own vehicles. While it might be possible to use complex mathematical formulas by hand to compute the best solution, computers transform the whole process into a routine task of feeding certain information as input and obtaining suggestions for best solutions as output. The task can typically be done in a few minutes (instead of hours or even days) and it becomes possible to examine several alternatives before deciding upon one that seems most realistic. Identifying problems and analysing the factors that cause them also has been transformed by modern computer information systems. In a typical MIS environment, standard reports are generated in a routine manner comparing actual performance against original estimates. The software that generates the report can be instructed to highlight exceptions, i.e. significant variations between original estimates and actual performance. Managers will thus become aware of problem areas in the daily course of their work simply by looking at the reports they receive, without having to do detailed data collection and computations themselves. Identifying the factors responsible for the problem can also be routinized to some extent by using such tools as variance analysis. Variance analysis is an element of standard costing system that splits deviations from estimates (or standards) into causative factors such as increase in price of materials used, excessive usage of materials, unexpected machine downtimes, etc. With such a detailed report, managers can delve deeper into the problem factor, such as why there was excessive usage of materials. Control is also exercised through variance analysis. Budgets are prepared for all business operations by concerned managers working in a coordinated fashion. For example, estimated sales volumes will determine the levels of production; production levels will determine raw material purchases; and so on. With good information system management, it then becomes possible to generate timely reports comparing actual sales, production, raw material deliveries, etc against estimated levels. The reports will help managers to keep a watch on things and take corrective action quickly. For example, the production manager will become aware of falling sales (or rising sales) of particular products and can prepare to make adjustments in production schedules, and purchasing and inventory managers will become quickly aware of any mounting inventories of unused materials. MIS thus enhances the quality of communication all around and can significantly improve the effectiveness of operations control. Effective MIS Involves Humans and Computers Working together The major aspect to note is that MIS provides only the information; it is the responsibility of concerned managers to act on the information. It is the synergy between efficient, accurate and speedy equipment and humans with common sense, intelligence and judgment that really gives power to MIS. As a chartered accountant with business management qualifications and decades of exposure to business in senior to top level positions, Gopinathan helps small to medium businesses with new business start-up, business performance improvement and marketing. He uses structured business modelling techniques to help with all these, and has launched a business support website to provide the help in a convenient manner irrespective of geographical distances and boundaries. Significance Information technology has grown to permeate the business world, affecting how companies make and market their products, as well as how people communicate and accomplish their jobs in modern organizations. Specialized software shapes best-practices and industry standards, continually changing the face of business in almost every way. Information technology management includes many of the basic functions of management, such as staffing, organizing, budgeting and control, but it also has functions that are unique to IT, such as software development, change management, network planning and tech support. Generally, IT is used by organizations to support and compliment their business operations. The advantages brought about by having a dedicated IT department are too great for most organizations to pass up. Some organizations actually use IT as the centre of their business. Positive Impact on Business: First off, I.T. affects how businesses go about with their usual routine. For instance: the technology allows companies to go paperless, depending only on digital databases to store important data. Many pundits see this as a risk; isn’t digital data flimsy and unreliable, they ask. But the shift offers several benefits as well—including reduction in operational cost, since the business no longer has to buy or rent space or equipment just to store data. Information Technology also affects the accuracy of business operations. When a company uses a computerized accounting system instead of relying on a real live accountant, they eliminate (or, at the very least, significantly reduce) human error. And because such systems allow for faster operations, the company’s workers can concentrate on more pressing tasks. The impact of the Management Information System The impact of MIS on the functions is in its management. With a good MIS support, the management of marketing, finance, production and personnel becomes more efficient, the tracking and monitoring the functional targets becomes easy. The functional managers are informed about the progress, achievements and shortfalls in the activity and the targets. The manager is kept alert by providing certain information indicating the probable trends in the various aspects of business. This helps in forecasting and long-term perspective planning. The manager† attention is brought to a situation which is exceptional in nature, inducing him to take an action or a decision in the matter. A disciplined information reporting system creates a structured database and a knowledge base for all the people in the organization. The information is available in such a form that it can be used straight away or by blending and analysis, saving the manager’s valuable time. The MIS creates another impact in the organization which relates to the understanding of the business itself. The MIS begins with the definition of a data entity and its attributes. It uses a dictionary of data, entity and attributes, respectively, designed for information generation in the organization. Since all the information systems use the dictionary, there is common understanding of terms and terminology in the organization bringing clarity in the communication and a similar understanding of an event in the organization. The MIS calls for a systemization of the business operations for an effective system design. This leads to streamlining of the operations which complicate the system design. It improves the administration of the business by bringing a discipline in its operations everybody is required to follow and use systems and procedures. This process brings a high degree of professionalism in the business operations. Since the goals and objective of the MIS are the products of business goals and objectives, it helps indirectly to pull the entire organization in one direction towards the corporate goals and objectives by providing the relevant information to the people in the organization. A well designed system with a focus on the manager makes an impact on the managerial efficiency. The fund of information motivates an enlightened manager to use a variety of tools of the management. It helps him to resort to such exercises as experimentation and modelling. The use of computers enables him to use the tools and techniques which are impossible to use manually. The ready-made packages make this task simpler. The impact is on the managerial ability to perform. It improv es the decision making ability considerably. Communications Technology Leveraging advances in communications technology is imperative to surviving in the modern business world. Advances in cellular phone technology have revolutionized the way businesspeople communicate with clients, employees, suppliers and strategic partners. The Internet has revolutionized the marketing function in addition to opening up a wide range of communication options. Modern smartphones are changing the game yet again with the introduction of new and innovative applications. A small business owner can now access a web-based customer relationship management service on a smartphone from anywhere in the world, for example, allowing him to obtain vital data about contacts before making calls. Accessibility By making communication more convenient, communication technology has helped to make communication more accessible, especially long-distance communication. Through various computer-mediated communication methods–communication done through the use of a computer, such as email, instant messages, and social networking websites–you can easily and instantly communicate with people in other cities, states and countries. This is vastly different from early forms of long-distance communication. Early forms of long-distance communication included the use of homing pigeons and/or runners to carry a message to its destination. How long a message would take before reaching its destination depended on the distance between the person sending the message and the person receiving the message. Mass Communication Organizations, like schools and businesses, use electronic communication to share information with a large number of people. Businesses can send mass emails to employees in order to inform workers about things like rule changes and important meetings. Many academic institutions use mass communication in order to maintain a well-informed campus. In case of an emergency, such as a spreading fire or presence of an armed assailant, mass communication can quickly inform people of the situation, giving enough notice to allow people to take the necessary actions to stay safe. Computer-mediated communication can be socially beneficial. For people who are socially awkward, computer-mediated communication such as online forums and chat rooms can help create a more fulfilling social life. For example, someone who fails at personal relationships because he speaks before thinking about the consequences has more control with Internet communication. He can read back his statements before posting, which will likely reduce the number of awkward moments the person creates. Communication technology offers other benefits, as well. Dating websites, for instance, can relieve some of the frustration involved with dating by allowing you to view the profiles of potential dates so you can determine who is the best match for you. This can save you both time and heartbreak. Considerations While information technology solutions can contribute to the success of your organization, there are a number of unique costs to consider. In addition to the cost of implementing an IT solution, you must employ highly educated and specialized workers to maintain, monitor, expand and repair your IT infrastructure. Question 4 : The role of hospital has changed from medical assistance to health care. What are the important information systems required to be considered while implementing front-end application development for hospital management? HOSPITAL INFORMATION SYSTEM (HIS) A hospital information system (HIS) is essentially a computer system that can manage all the information to allow health care providers to do their jobs effectively. These systems have been around since they were first introduced in the 1960s and have evolved with time and the modernization of healthcare facilities. The computers were not as fast in those days and they were not able to provide information in real time as they do today. The staff used them primarily for managing billing and hospital inventory. All this has changed now, and today hospital information systems include the integration of all clinical, financial and administrative applications. Modern HIS includes many applications addressing the needs of various departments in a hospital. They manage the data related to the clinic, finance department, laboratory, nursing, pharmacy and also the radiology and pathology departments. The hospitals that have switched to HIS have access to quick and reliable information including patients’ records illustrating details about their demographics, gender, age etc. By a simple click of the mouse they receive important data pertaining to hospital finance systems, diet of patients, and even the distribution of medications. With this information they can monitor drug usage in the facility and improve its effectiveness. Many hospitals have as many as 200 disparate systems combined into their HIS. Hospital information systems have become very advanced and new innovations are continuously being introduced. But a HIS is useless if it confuses the hospital employees. The system must be user friendly and should include training by the vendors. A good HIS offers numerous benefits to a hospital including but not limited to the delivery of quality patient care and better financial management. The HIS should also be patient centric, medical staff centric, affordable and scalable. The technology changes quickly and if the system is not flexible it will not be able to accommodate hospital growth. COMPONENTS It can be composed of one or a few software components with specialty-specific extensions as well as of a large variety of sub-systems in medical specialties * Laboratory Information System (LIS) * Radiology Information System (RIS) * Clinical Information System (CIS) * Nursing Information Systems (NIS) * Pharmacy Information System (PIS) SOFTWARE COMPONENT Software Component is a system element offering a predefined service or event, and able to communicate with other components. It should be : * Multiple-use * Non-context-specific * Compostable with other components (inter relationship with other components) * Encapsulated i.e., non-investigable through its interfaces * A unit of independent deployment and versioning Laboratory Information System (LIS) Laboratory Information System (LIS) is a software based laboratory and information management system that offers a set of key features that support a modern laboratory’s operations. Those key features include but are not limited to workflow and data tracking support, flexible architecture, and smart data exchange interfaces, which fully support its use in regulated environments. The features and uses of a LIMS have evolved over the years from simple sample tracking to an enterprise resource planning tool that manages multiple aspects of laboratory informatics. Due to the rapid pace at which laboratories and their data management needs shift, the definition of LIMS has become somewhat controversial. As the needs of the modern laboratory vary widely from lab to lab, what is needed from a laboratory information management system also shifts. The end result: the definition of a LIMS will shift based on who you ask and what their vision of the modern lab. Radiology Information System (RIS) A radiology information system (RIS) is a networked software suite for managing medical imagery and associated data. An RIS is especially useful for managing radiological records and associated data in a multiple locations and is often used in conjunction with a picture archiving and communication system (PACS) to manage work flow and billing. An RIS has several basic functions: Patient management * An RIS can track a patient’s entire workflow within the radiology department; images and reports can be added to and retrieved from electronic medical records (EMRs) and viewed by authorized radiology staff. Scheduling * Appointments can be made for both in- and out-patients with specific radiology staff. Patient tracking * A patient’s entire radiology history can be tracked from admission to discharge. The history can be coordinated with past, present and future appointments. Results reporting * An RIS can generate statistical reports for a single patient, group of patients or particular procedure. Film tracking * An RIS can track individual films and their associate data. Billing * An RIS facilitates detailed financial record-keeping, electronic payments and automated claims submission. Clinical Information System (CIS) Clinical Information System is a collection of various information technology applications that provides a centralized repository of information related to patient care across distributed locations. This repository represents the patient’s history of illnesses and interactions with providers by encoding knowledge capable of helping clinicians decide about the patient’s condition, treatment options, and wellness activities. The repository also encodes the status of decisions, actions underway for those decisions, and relevant information that can help in performing those actions. The database could also hold other information about the patient, including genetic, environmental, and social contexts. Features : * access the medical literature * ask clinical or administrative questions of aggregates of patient data, * receive automatic warnings or suggestions when the patient’s data satisfy certain logical rules * receive critiques when proposing therapies or ordering diagnostic tests, * access guidelines for standards of care * analyse trade-offs and the likelihood of alternative outcomes (decision analysis) * receive lists of differential diagnoses Nursing Information System (NIS) Nursing information systems is a type of health care management system. It helps nurses use their nursing skills and computer knowledge within a health care environment. A nursing information system has different features and benefits. Features Nurses can self-schedule work hours based on their work shift and departmental needs. Staff nurse managers or nursing administrators can review each nurse’s schedule and make approvals. Another nursing information system feature includes documenting patient care plans. Benefits One nursing information system benefit includes saving time. For instance, nursing managers can confirm a nurse’s work availability without contacting each nurse. Nurses can review a patient’s treatment plan when working outside the office, such as a home care nurse. Pharmacy Information System (PIS) Pharmacy information systems (PIS) are complex computer systems that have been designed to meet the needs of a pharmacy department. Through the use of such systems, pharmacists can supervise and have inputs on how medication is used in a hospital. Some of the activities which Pharmacy Information Systems have been employed in pharmacy departments include: Clinical Screening The Pharmacy Information System can assist in patient care by the monitoring of drug interactions, drug allergies and other possible medication-related complications. When a prescription order is entered, the system can check to see if there are any interactions between two or more drugs taken by the patient simultaneously or with any typical food, any known allergies to the drug, and if the appropriate dosage has been given based on the patient’s age, weight and other physiologic factors. Alerts and flags come up when the system picks up any of these. Prescription Management The Pharmacy Information System can also be used to manage prescription for inpatients and/or outpatients. When prescription orders are received, the orders are matched to available pharmaceutical products and then dispensed accordingly depending on whether the patient is an inpatient or outpatient. It is possible to track all prescriptions passed through the system from who prescribed the drug, when it was prescribed to when it was dispensed. It is also possible to print out prescription labels and instructions on how medication should be taken based on the prescription. Inventory Management Pharmacies require a continuous inventory culture in order to ensure that drugs do not go out of stock. This is made even more difficult when there are multiple dispensing points. When don manually it is very difficult to maintain an accurate inventory. Pharmacy Information Systems aid inventory management by maintaining an internal inventory of all pharmaceutical products, providing alerts when the quantity of an item is below a set quantity and providing an electronic ordering system that recommends the ordering of the affected item and with the appropriate quantity from approved suppliers. Patient Drug Profiles These are patient profiles managed by the Pharmacy Information System and contain details of their current and past medications, known allergies and physiological parameters. These profiles are used for used for clinical screening anytime a prescription is ordered for the patient. Report Generation Most Pharmacy Information Systems can generate reports which range from determining medication usage patterns in the hospital to the cost of drugs purchased and /or dispensed. Interactivity with other systems It is important that Pharmacy Information Systems should be able to interact with other available systems such as the clinical information systems to receive prescription orders and financial information system for billing and charging.