The impacts of implementing a data warehouse in the banking industry Data warehousing in the financial sector Introduction In the modern banking and financial sector‚ there is keener and stronger competition and many enterprises are much more eager to get immediate and accurate information to make better and faster decisions. Furthermore‚ with many banks fighting to capture new customers and the rapidly growing need for larger amounts and more specific information‚ traditional databases are incapable
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and Result Interpretation 4.4.2.1 Effectiveness Criteria Results 1. Visual Promethee-based Effectiveness Analysis Visual Promethee main window is displayed that uses a typical spreadsheet to manage the data of effectiveness multi-criteria problem (Figure 4.7). The main window contain all the data have related to the PROMETHEE method (preference function‚ statistics and evaluation‚ weights…)‚ this information can be easily input and defined by the decision maker.in addition to that Visual PROMETEE
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Knowledge in Data For each of the following meetings explain which phase in CRISP-DM process is represented: a. Managers want to know by next week whether deployment will take place. Therefore analysts meet to discuss how useful and accurate their model is. This is the Evaluation phase in the CRISP-DM process. In the evaluation phase the data mining analysts determine if the model and technique used meets business objectives established in the first phase. b. The data mining project manager
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Battle between Hadoop and Data Warehouse #1 - Introduction Once or twice every decade‚ the IT marketplace experiences a major innovation that shakes the entire IT industry. In recent years‚ Apache Hadoop has done the same thing by infusing data centres with new infrastructure By giving the power of parallel processing to the programmer Hadoop is on such an exponential rise in adoption and its ecosystem is expanding in both depth and breadth‚ it is natural to ask whether Hadoop’s is going to replace
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Abstract: The existing business paradigm of data analytics is set for a transformation. Today‚ companies are experimenting to replicate the “Outsourced data analytics” model to “Crowdsourced data analytics”. Companies like Kaggle‚ Crowdanalytix and others are hitting the headlines of top analytics blogs across the globe. The reason is that the new business model promises a drastic decrease in the cost of analytics for companies long with the flexibility to get the problem solved anytime with much
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2012 MODERN APPROACH TO BROWSER BASED DATA VISUALIZATION Background: Visualization purpose is the communication of data where data must come from something that is abstract or at least not immediately visible which also involves photography and image processing. This project refers to the visual presentation of data information that is extracted from schematic form which includes attributes or variables for the unit of information and where the data presentation is implementated using a web
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Components of DSS (Decision Support System) Data Store – The DSS Database Data Extraction and Filtering End-User Query Tool End User Presentation Tools Operational Stored in Normalized Relational Database Support transactions that represent daily operations (Not Query Friendly) Differences with DSS 3 Main Differences Time Span Granularity Dimensionality Operational DSS Time span Real time Historic Current transaction Short time frame Long time frame Specific Data facts Patterns Granularity Specific
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Data Mining Weekly Assignment 6: LIFT; CRM; AFFINITY POSITIONING; CROSS-SELLING AND ITS ETHICAL CONCERNS. What is meant by the term “lift”? The term “lift” describes the improved performance of an exact or specific amount of effort on a modeled sampling‚ as opposed to a random sampling (Spang‚ 2010). In other words‚ if you are able to market via a model to say‚ a given number of random customers (e.g. 1000)‚ and we expect that 50 of them would be successful‚ then a model that can generate 75
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Data mining Data mining is simply filtering through large amounts of raw data for useful information that gives businesses a competitive edge. This information is made up of meaningful patterns and trends that are already in the data but were previously unseen. The most popular tool used when mining is artificial intelligence (AI). AI technologies try to work the way the human brain works‚ by making intelligent guesses‚ learning by example‚ and using deductive reasoning. Some of the more popular
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1. What business and social problems does data center power consumption cause? Data center power consumption economically affects businesses and environmentally affects society. Operating costs for data centers is very expensive. In the article‚ "Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments‚" the total operating costs‚ concerning electricity‚ of all data centers within the U.S. alone exceeded 7 billion dollars in 2010 (Ayala‚ J.‚ Moya‚ J.‚ Risco-Martín
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