Data warehouse and data mining

entropy storage w atomic number 18ho purpose and selective selective learning exploitAbstract information exploit and info w beho determination is ace of an important issue in a corporate world today. The biggest contest in a world that is full of information is searching through it to find connections and entropy that were non previously known. Dramatic advance in entropy development carry the role of entropy minelaying and info w behouse become important in order to improve employment operating room in transcription. The scenarios of important selective information exploit and entropy w behouse in institution are seen in the process of accumulating and integrating of grand and growing amounts of information in various format and various informationbases. This publisher is discuss about entropy store and information minelaying, the concept of entropy tap and selective information storage warehouse, the joyrides and techniques of informa tion exploit and also the benefits of entropy mining and selective information warehouse to the institutions.Keywords information, info Warehouse, info mining, entropy Mart knowledgeablenessOrganizations tend to grow and prosper as they gain a crack understanding of their environment. Typic wholey, parentage managers mustiness be able to track perfunctory transactions to evaluate how the communication channel is performing. By tapping into the operational selective informationbase, management poop develop strategies to meet organizational goals. The process that identify the trends and patterns in information are the factors to touch that. By the way, the way to handle the operational information in organization is important because the reason for generating, storing and managing selective information is to become information that becomes the basis for rational purpose qualification. To facilitate the close-making process, purpose support systems (DSSs) wer e develop whereas it is an arrangement of computerized tools employ to assist managerial conclusiveness making within a melodic line. Decision support is a methodological compendium that designed to chicken out information from information and to use such information as a basis for decision making. However, information requirements have become so knotty that is difficult for a DSS to extract all necessary information from the data structures typically found in an operational database. Therefore, a data mining and data warehouse was developed and become a proactive methodology in order to support managerial decision making in organization.Concept of Data WarehouseA data warehouse is a firms repositories that running the process of updating and storing historical business data of organization whereas the process then transform the data into multidimensional data mannequin for good querying and synopsis. All the data stored are extracts or obtains its data from octuple op erational systems in organization with containing the information of relevant activity that occurred in the past in order to support organizational decision making. A data mart, on the early(a) hand, is a sub stigmatise of a data warehouse. It holds or so special information that has been grouped to help business in making better decisions. Data used here are usually derived from data warehouse. The first organized used of such large database started with OLAP (Online Analytical Processing) whereas the rivet is analytical impact of organization. The diffrences between a data mart and a data warehouse is only the size and scope of the problem universe solved.According to William H.Inmon (2005), a data warehouse is a subject-oriented, integrated, snip-varying, and non-volatile collection of data in support of the managements decision-making process. To understand that definition, the comp wizardnts will be explained more than(prenominal) expositIntegratedProvide a unified vie w of all data elements with a common definition and representation for all business units.Subject-orientedData are stored with a subject orientation that facilitates multiple views of the data and facilitates decision making. For example, sales whitethorn be recorded by product, by division, by manager, or by region.Time-variantDates are recorded with a historical place in mind. Therefore, a clip dimension is added to facilitate data analysis and various time comparisons.NonvolatileData send wordnot be changed. Data are added only periodically from historical systems. Once the data are the right wing way stored, no changes are throw overboarded. Therefore, the data environment is relatively static.In summary, the data warehouse is usually a read-only database optimized for data analysis and query processing. Typically, data are extracted from various sources and are then transformed and integrated, in other words, passed through a data filter, before being loaded into the data warehouse. Users access the data warehouse via front-end tools and end-user application software to extract the data in usable form.The Issues That Arise in Data WarehouseAlthough the change and integrated data warehouse can be a really attractive proposition that yields m whatsoever benefits, managers may be reluctant to heart this strategy. Creating a data warehouse requires time, m unitaryy, and considerable managerial effort. Therefore, it is not move that m both companies begin their foray into warehousing by focusing on more manageable data sets that are targeted to meet the special demand of small groups within the organization. These smaller data warehouse are called data marts. A data mart is a small, single-subject data warehouse subset that provides decision support to a small group of people. Some organizations choose to accomplish data marts not only because of the lower cost and shorter implementation time, barely also because of the current technological advanc es and inevitable people issues that make data marts attractive. Powerful computers can provide a customized DSS to small groups in ways that office not be possible with a of importized system. Also, a follows culture may predispose its employees to resist major changes, but they might quickly embrace relatively minor changes that lead to demonstrably modify decision support. In addition, people at different organizational levels are promising to require data with different summarization, aggregation, and presentation formats. Data marts can serve as a test vehicle for companies exploring the potential benefits of data warehouses. By migrating gradually from data marts to data warehouses, a specific departments decision support needs can be addressed within a reasonable time frame ( vi month to unity year), as compared to the eternal time frame usually required to implement a data warehouse (one to three geezerhood). Information Technology (IT) departments also benefit from this plan of attack because their personnel have the opportunity to learn the issues and develop the skills required to create a data warehouse.Concept of Data archeological siteData mining is the forecasting techniques and analytical tools that extensively used in industries and corporates to ensure the military capability in decision making. Data mining is a tools to analyze the data, show problems or opportunities hidden in the data relationships, form computer places base on their findings, and then use the models to harbinger business behavior by requiring minimal end-user intervention. The way it works is through search of valuable information from a huge amount of data that is collected over time and defined the patterns or relationships of information that present by data. In business field, the organization use data mining to predict the customer deportment in the business environment. The process of data mining started from analyzed the data from different perspec tives and summarized it into useful information, which from the information then created knowledge to address any number of business problems. For the example, banks and book of facts card companies use knowledge-based analysis to get fraud, thereby decreasing double-faced transactions. In fact, data mining has turn out to be genuinely helpful in finding practical relationships among data that help define customer buying patterns, improve product development and acceptance, reduce healthcare fraud, analyze stock markets and so on.Data Mining in Historical PerspectiveOver the last 25 years or so, there has been a gradual evolution from data processing to data mining. In the 1960s business routinely collected data and processed it using database management techniques that allowed an orderly listing and tabulation of the data as well as some query activity. The OLTP (Online Transaction Processing) became routine, data retrieval from stored data bacame faster and more efficient bec ause of the availability of stark naked and better storage devices, and data processing became quicker and more efficient because of advancement in computer technology. Database management advanced rapidly to involve highly sophisticated query systems, and became popular not only in business applications but also in scientific inquiries.Approaches of Data Mining in Various IndustriesWith data mining, a retail store may find that sure products are sold more in one channel of distribution than in the others, certain products are sold more in one geographical location than in others, and certain products are sold when a certain event occurs. With data mining, a pecuniary analyst would bid to know the characteristics of a successful prospective employee quote card departments would like to know which potential customers are more likely to pay back the debt and when a credit card is swiped, which transaction is fraudulent and which one is legitimate direct marketers would like to k now which customers acquire which types of products booksellers like Amazon would like to know which customers purchase which types of books (fiction, detective stories or any other kind) and so on. With this type of information available, decision makers will make better choices. Human resource people will hire the right individuals. Credit departments will target those prospective customers that are less addicted to become delinquent or less likely to involve in fraudulent activities. Direct marketers will target those customers that are likely to purchase their products. With the insight gained from data mining, businesses may wish to re-configure their product offering and show specific causes of a product. These are not the only uses of data mining. guard use this tool to bound when and where a crime is likely to occur, and what would be the nature of that crime. Organized stock changes detect fraudulent activities with data mining. pharmaceutical companies mine data to predict the efficacy of compounds as well as to uncover parvenue chemical entities that may be useful for a particular disease. The airline industry uses it to predict which flights are likely to be delayed (well before the flight is scheduled to depart). Weather analyst determine weather patterns with data mining to predict when there will be rain, sunshine, a hurricane, or snow. Beside that, nonprofit companies use data mining to predict the likelihood of individuals making a donation for a certain cause. The uses of data mining are far reaching and its benefits may be quite a significant.Data Mining Tools and TechniquesData mining is the set of tools that learn the data obtained and then using the useful information for business forecasting. Data mining tools use and analyze the data that exist in databases, data marts, and data warehouse. A data mining tools can be categorized into foursome categories of tools which are prediction tools, classification tools, clustering analy sis tools and association rules discovery. at a lower place are the elobaration of data mining toolsPrediction ToolsA prediction tool is a method that derived from traditional statistical forecasting for predicting a harbor of the variable.Classification ToolsThe classification tools are attempt to distinguish the differences between classes of objects or actions. Given the example is an advertiser may want to know which perspective of its promotion is most appealing to consumers. Is it a price, quality or dependableness of a product? Or maybe it is a special feature that is missing on competitive products. This tools help give such information on all the products, making possible to use the advertising reckon in a most effective manner.Clustering Analysis ToolsThis is very powerful tools for clustering products into groups that naturally fall together which are the groups are identified by the programme. Most of the clusters discovered may not be useful in business decision. However, they may find one or two that are extremely important which the ones the company can head advantage of. The most common use is market segmentation which in this process, a company divides the customer base into segments dependent upon characteristics like income, wealthiness and so on. Each segment is then treated with different merchandise approach.Association Rules DiscoveryThis tool discover associations which are like what kinds of books certain groups of people read, what products certain groups of people purchase and so on. Businesses use such information in targeting their markets. For instance, recommends movies based on movies people have watched and rated in the past.There are four general patterns in data mining which are data preparation, data analysis and classification, knowledge acquisition and medical prognosis.Data PreparationIn the data preparation phase, the main data sets to be used by the data mining operation are identified and cleaned of any data impurities. Because the data in the data warehouse are already integrated and filtered, the data warehouse usually is the target set for data mining operations.Data AnalysisThe data anlysis and classification phase studies the data to identify common data characteristics or patterns. During this phase, the data mining tool applies specific algorithm to findData groupings, classifications, clusters, or sequences.Data dependencies, links, or relationships.Data patterns, trends, and deviations.Knowledge AcquisitionThe knowledge-acquisition phase uses the results of the data analysis and classification phase. During the knowledge-acquisition phase, the data mining tool (with possible intervention by the end user) selects the appropriate modeling or knowledge-acquisition algorithms. The most common algorithms used in data mining are based on flighty networks, decision trees, rules induction, genetic algorithms, classification and regression trees, memory-based reasoning, and nearest ne ighbor and data opticization. A data mining tool may use numerous of these algorithms in any combination to generate a computer model that reflects the behavior of the target data set. PrognosisAlthough many data mining tools stop at the knowledge-acquisition phase, others continue to the prognosis phase. In that phase, the data mining findings are used to predict future behavior and forecast business outcomes. Examples of data mining findings can be65% of customers who did not use a particular credit card in the last six months are 88% likely to cancel that account.82% of customers who bought a 27-inch or larger TV are 90% likely to buy an frolic center within the next four weeks.If age 30 and income = 25,000 and credit rating 25,000, then the minimum loan term is ten years.The complete set of findings can be represented in a decision tree, a neural net, a forecasting model, or a visual presentation interface that is used to project future events or results. For example, the prognosis phase might project the likely outcome of a new product rollout or a new marketing promotion.The Benefit and Weaknesess of Data Warehouse to OrganizationData warehouse is the one of powerful techniques that applies in organization in order to assist managerial decision making within a business. This methodology becomes a crucial asset in modern business enterprise. It is designed to extract information from data and to use such information as a basis for decision making. The organization will get more benefit with application of data warehouse because the features of data warehouse itself is its a central repositories that stores historical information, meaning arrange that eventhough the data come from differ location and various points in time but all the relevant data are assembled in one location and was organized in efficient manner. Indirectly, it makes a profit to company because it greatly reduces the computing cost. One of the advantage of using data warehouse i s it allows the accessible of large volume information whereas the information will be used in problem solving that arise in business organization. All the data that are from multiple sources that located in central repository will be analyze in order to allow them come out with a choice of solutions.However there are also having weaknesses that need to concern as well. The processes of data warehouse in reality take a long period of time bacause before all the data can be stored into warehouse, they need to cleaned, extracted and loaded. The process of maintaining the data is one of the problems in data warehouse because it is not easy to handle. The compatibility may be the isssued in order to implement the data warehouse in organization because the new transaction system that tried to implement may not work with the system that already used. Beside that, the user that works with the system must be trained to use the system because without having a proper training may cause a pro blem. Furthermore, if the data warehouse can be accessed via the internet, the security problem might be the issue. The biggest problem that related with the data warehouse is the costs that must taken into consideration especially for their maintenance. any organization that is considering using a data warehouse must define if the benefits outweigh the costs.ConclusionSuccessfully supporting managerial decision-making is significantly dependent upon the availability of integrated, high quality information organized and presented in a timely and in simply way to understand. Data mining and data warehouse have emerged to meet this need. The application of data mining and data warehouse will be apart of crucial element in organization in order to assist the managerial running the operation smoothly and at the same time will help them to accomplish the business goal. It is because both of these techniques are the foundation of decision support system. at present data mining and data warehouse are an important tools and more companies will begin using them in the future. REFERENCESBonifati, A., Cattaneo, F., Ceri, F., Fuggetta, A., and Paraboschi, S., (2001). Designing data marts for data warehouse. ACM Transactions On Software Engineering And Methodology, 10, 452-483. Retrieved February 15, 2010 from http//www.emeraldinsight.com.ezaccess.library.uitm.edu.my/Insight/viewPDF.jsp?contentType=ArticleFilename=html/yield/ print/EmeraldAbstractOnlyArticle/Pdf/2810110103.pdfChaplot, P., (2007). An introduction to data warehousing. 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