...According to Lee, the most popular definition is a data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process (2014). Basically a data warehouse is a copy of transaction data specifically structured for query and analysis. According to Frand, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information – information that can be used to increase revenue, cut costs, or both (1997). There are many benefits of data warehousing. Yes, it will cost large amounts of money from businesses to have a data warehouse but, in the long run it is worth it to have in a corporation. One benefit is that data warehouses stores and presents information in a way that allows management to make important decisions (Prathap, 2014). Management and even executives can look at the business as a whole instead of by each department. According to Prathap, another benefit of data warehouses is their ability to handle server tasks connected to querying which is not used in most transaction systems (2014). Creating queries and reports can take time and with data warehousing, the server can handle the tasks in a timely fashion. Again, according to Prathap, one of the most important benefits of data warehouses is that they set the stage for an environment where a small amount of technical knowledge about databases...
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...Data mining and warehousing and its importance in the organization * Data Mining Data mining is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Data mining is primarily used today by companies with a strong consumer focus - retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among internal factors such as price, product positioning, or staff skills, and external factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to drill down into summary information to view detail transactional data. For example, “Entertainers Incorporated” is an organization which deals with entertainers for events. So the need to attract customers and communicating with them is essential. Customer satisfaction in their service is much needed for them, for the customers to approach them for the next event too. So considering all...
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...Introduction 2 Assumptions 3 Data Availability 3 Overnight processing window 3 Business sponsor 4 Source system knowledge 4 Significance 5 Data warehouse 6 ETL: (Extract, Transform, Load) 6 Data Mining 6 Data Mining Techniques 7 Data Warehousing 8 Data Mining 8 Technology in Health Care 9 Diseases Analysis 9 Treatment strategies 9 Healthcare Resource Management 10 Customer Relationship Management 10 Recommended Solution 11 Corporate Solution 11 Technological Solution 11 Justification and Conclusion 12 References 14 Health Authority Data (Appendix A) 16 Data Warehousing Implementation (Appendix B) 19 Data Mining Implementation (Appendix B) 22 Technological Scenarios in Health Authorities (Appendix C) 26 Technology Tools 27 Data Management Technology Introduction The amount of information offered to us is literally astonishing, and the worthiness of data as an organizational asset is widely acknowledged. Nonetheless the failure to manage this enormous amount of data, and to swiftly acquire the information that is relevant to any particular question, as the volume of information rises, demonstrates to be a distraction and a liability, rather than an asset. This paradox energies the need for increasingly powerful and flexible data management systems. To achieve efficiency and a great level of productivity out of large and complex datasets, operators need have tools that streamline the tasks of managing the data and extracting valuable...
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...Data Mining/Data Warehousing Matthew P Bartman Strayer University Ibrahim Elhag CIS 111– Intro to Relational Database Management June 9, 2013 Data Mining/Data Warehousing When it comes to technology especially in terms of storing data there are two ways that it can be done and that is through data mining and data warehousing. With each type of storage there are trends and benefits. In terms of data warehousing there are 5 key benefits one of them being that it enhance business intelligence. What this means is that business processes can be applied directly instead of things having to be done with limited information or on gut instinct. Another benefit of data warehousing is that it can also save time meaning that if a decision has to be made the data can be retrieved quickly instead of having to find data from multiple sources. Not only does data warehousing enhance business intelligence and save time but it can also enchance data quality and consistency.This is accomplished by converting all data into one common format and will make it consistent with all departments which ensures accuracy with the data as well. While these key benefits another one is that it can provide historical intelligence which means that analayze different time periods and trends to make future predictions. One other key benefit is that it provides a great return on investment. The reason being that a data warehouse generates more revenue...
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...how can data warehousing, data mining & predictive analytics improve a business. Would it be applicable to all types of business or a particular business only? Information Technology develops rapidly, because changes in these technologies are making the people’s lives easier. There’s a growing need for information in market and the competition of handling information. Some businesses needs to improve their ability and capability to handle big data or information. Data warehousing evolved and plays a big or essential role in the storage, information management and to support strategic reporting and analytics of companies. Businesses are investing to integrate their daily operations to be contained in their data warehouse. Businesses aims for a growth to their competitive advantage compared to other organizations. Some of these competitive advantages includes data warehousing, data mining and predictive analytics to be applied with effective use of Information Technology. Data warehouse is designed to support decision making for leaders or owners of an organization. Data warehouse is truly important for which it gives or share all data by every department of an organization that allows decision making in order to achieve good analysis which will help better the organization’s business situation to improve their current operational processes. Data mining is a process that assists data warehouse to dig and analyze big sets of data and extracting the data. It allows...
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...3) Abstract Data is the most valuable enterprise asset, and a properly integrated data management strategy will enhance an organization’s ability to develop valuable insights that will provide greater business value. More specifically, data management is the development and execution of a company’s procedures, policies and architectures: in order to better manage its informational needs in an effective and efficient manner. Data warehousing, online transactional databases, and data mining can solve or reduce difficulties associated with managing data. Solving Data Management Difficulties The concept of data warehousing is to create a central location and permanent storage space for the various data sources needed to support a company’s analysis and reporting. In other words, it is a database that focuses on query and analysis rather than actual transaction processing (Reeves, 2009). It usually contains historical data derived from a company’s transaction data, but may include data from several other sources. Data warehouses separate analysis workload from transaction workload and enable an organization to consolidate data from several sources. Since executives and managers can quickly and efficiently access data from a multiple sources, they are able to make informed decisions on key initiatives promptly: rather than waste time retrieving data from multiple sources. Another benefit of data warehousing is that it stores large amounts of historical data so it can be analyzed...
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...middleware, and industry standards as relating to the enterprise data repository. Data warehousing, data mining, and data marts are covered from an enterprise perspective. Policies Faculty and students will be held responsible for understanding and adhering to all policies contained within the following two documents: • • University policies: You must be logged into the student website to view this document. Instructor policies: This document is posted in the Course Materials forum. University policies are subject to change. Be sure to read the policies at the beginning of each class. Policies may be slightly different depending on the modality in which you attend class. If you have recently changed modalities, read the policies governing your current class modality. Course Materials Coronel, C., Morris, S., & Rob, P. (2011). Database systems: Design, implementation and management (9th ed.). Mason, OH: Cengage Learning. Eckerson, W. W. (2011). Performance dashboards: Measuring, monitoring, and managing your business (2nd ed.). Hoboken, NJ: John Wiley & Sons, Inc. Hoffer, J. A., Ramesh, V., & Topi, H. (2011). Modern database management (10th ed.). Upper Saddle River, NJ: Pearson. Linoff, G. S., & Berry, M. J. A. (2011). Data mining techniques: For marketing, sales, and customer relationship management (3rd ed.). Indianapolis, IN: Wiley Publishing, Inc. Ponniah, P. (2010). Data warehousing: Fundamentals for IT professionals (2nd ed.). Hoboken, NJ: John Wiley ...
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...head: SHORT TITLE OF PAPER (<= 50 CHARACTERS) Data Warehousing and Data Mining Bruce Nimo CIS 111 March 19, 2012. Prof Jones Data mining is a process of numerical analysis. Analysts use technical tools to query and sort through terabytes of data looking for patterns. Usually, the analyst will develop a hypothesis, such as customers who buy product X usually buy product Y within six months. Running a query on the relevant data to prove or disprove this theory is data mining. Data warehousing describes the process of designing how the data is stored in order to improve reporting and analysis. Data warehouse experts consider that the various stores of data are connected and related to each other conceptually as well as physically. A business's data is usually stored across a number of databases. However, to be able to analyze the broadest range of data, each of these databases needs to be connected in some way. This means that the data within them need a way of being related to other relevant data and that the physical databases themselves have a connection so their data can be looked at together for reporting purposes. Businesses then use this information to make better business decisions based on how they understand their customers' and suppliers' behaviors. Examples of businesses that use data warehousing and data mining are amason.com, Wal-Mart stores Inc etc. Both data mining and data warehousing are business intelligence tools that are used to turn information...
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...What is Data Warehousing? A data warehouse can be defined as follows: • subject oriented • integrated • time-variant • nonvolatile It is a collection of data in support of management decision-making process. Benefits of Data Warehousing Data warehousing is intended to support reporting and analysis of data. Here are the benefits as follows: • Potential High Returns on Investment • Competitive Advantage • Increased Productivity of Corporate Decision Makers Problems of Data Warehousing Here are some problems associated with developing and maintaining a data warehouse as follows: • Underestimation of Resources for Data Loading • Hidden Problems with Source Systems • Required Data not Captured • Required Data not Captured • Increased End User Demands • Data Homogenization • High Demand for Resources • Data Ownership • High Maintenance • Long Duration Projects • Complexity of Integration Data Warehouse Architecture Operational Data Store • A repository of current and integrated operational data used for analysis Load Manager • Performs all the operations associated with the extraction and loading of data into the extraction and loading of data into the warehouse Warehouse Manager • Performs all the operations associated with the management of data in the warehouse Query Manager • Performs all the operations associated...
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...Business Intelligence and Data Warehouses Kevin Gainey Mr. Brown CIS 111 Data warehouses support business decisions by collecting, consolidating, and organizing data for reporting and analysis with tools such as online analytical processing (OLAP) and data mining. Although data warehouses are built on relational database technology, the design of a data warehouse database differs substantially from the design of an online transaction processing system (OLTP) database. The topics in this paper address approaches and choices to be considered when designing and implementing a data warehouse. The paper begins by contrasting data warehouse databases with OLTP databases and introducing OLAP and data mining, and then adds information about design issues to be considered when developing a data warehouse with Microsoft® SQL Server™ 2000. A data warehouse supports an OLTP system by providing a place for the OLTP database to offload data as it accumulates, and by providing services that would complicate and degrade OLTP operations if they were performed in the OLTP database. Without a data warehouse to hold historical information, data is archived to static media such as magnetic tape, or allowed to accumulate in the OLTP database. If data is simply archived for preservation, it is not available or organized for use by analysts and decision makers. If data is allowed to accumulate in the OLTP so it can be used for analysis, the OLTP database continues to grow in size and...
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...MERCHANDISING – 27/02/2014 Data warehousing: * Data warehousing is the electronic storage of all info about current customers. * Traditionally this info was used for financial and accounting purposes only. * We now use this info for marketing purposes as well. * Information is primarily gathered via the credit card base of the retailer. * Data warehouses are also used as a single source for the retailer to view key performance indicators (sales, margin, stock, profit, etc.) * This involves the transference of data from operational systems (till points) to analysis reporting systems. * Info is available with immediate effect to decisions makers (buyers/planners) and suppliers. * Data warehousing results in: * Improved selection of merchandise * Stronger partnerships with suppliers * Successful promotions * Offering the right merchandise, @ right time, right people @ right place right people! * Implementing data warehouse is the easy part. The hard part is deriving meaning from the data. Data mining: * Involves searching through warehouse data to find trends and patterns. * Can be used to identify opportunities and threats in the company * Data mining is software capable of finding/ detecting meaningful patterns & relationships between figures. Can print specialized reports * However, it is up to PEOPLE to analyse the figures on the reports. General types of data produced by data mining: * Associations:...
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...Computer Networks Computer Graphics and Multimedia Lab Advanced Operating System Internet programming and Web Design Data Mining and Warehousing Internet programming and Web Design Lab Project Work and Viva Voce Total University Examinations Durations Max in Hrs Marks 3 100 3 100 3 100 3 100 3 100 3 3 3 3 100 100 100 100 100 1000 II For project work and viva voce (External) Breakup: Project Evaluation : 75 Viva Voce : 25 1 Anx.31 J - M Sc CS (SDE) 2007-08 with MQP Page 2 of 16 YEAR – I PAPER I: ADVANCED COMPUTER ARCHITECTURE Subject Description: This paper presents the concept of parallel processing, solving problem in parallel processing, Parallel algorithms and different types of processors. Goal: To enable the students to learn the Architecture of the Computer. Objectives: On successful completion of the course the students should have: Understand the concept of Parallel Processing. Learnt the different types of Processors. Learnt the Parallel algorithms. Content: Unit I Introduction to parallel processing – Trends towards parallel processing – parallelism in uniprocessor Systems – Parallel Computer structures – architectural classification schemes – Flynn’ Classification – Feng’s Classification – Handler’s Classification – Parallel Processing Applications. Unit II Solving problems in Parallel: Utilizing Temporal Parallelism – Utilizing Data Parallelism –...
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...important features of data mining tools Data mining is the process of fetching hidden information from huge databases for the purpose of analysis. Basically, it is a method to search for information that can prove to be useful for an organisation and to extract that knowledge from very lengthy and large databases. It uses a variety of statistical algorithms and analysis techniques to derive results. Although, this might sound easy but data mining is a lengthy process and requires loads of time and patience. It requires a lot of man-hours as an application can mine the data from the databases but it is the responsibility of the human to describe the data to look for to the application and also to find and collect the databases. (Naxton, n.d.) Analysis is key to outperforming your competition in today’s world. Almost all businesses rely on data to figure out the future market trends, know more about their customers and their preferences etc. An example of data mining is why companies advertise on Facebook as they get to reach a vast audience and learn about their habits. The information is derived from the advertisements the people click on, the time spent on that specific advert, the type of adverts they hide or like, and all this data is of value to companies to understand the market. Data mining comprises of 5 elements (“Data Mining—Why is it Important?,” n.d.): • “Extract, transform, and load transaction data onto the data warehouse system” • Store data in a MDB system to...
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...Data Warehouses and Data Mining Your Name DBM 384 May 13, 2013 Jim Cervi Data Warehouses and Data Mining Data warehouses serve an integral function within many different industries. In the government and law enforcement agencies this is especially prevalent. Vast amount of data and information from multiple sources is often collected by these agencies. This data and information must be put into a format that allows for workable details by the analysts (HowStuffWorks.com, 2012). Data mining and data warehouses provide these agencies with the ability to select specific data out of the large volumes of data available to the analyst Data Warehouse A data warehouse is a database of information collected from several resources, saved under a specific schema, at only one site according to (Siberschatz, Korth, & Sudarshan, 2011). This type of system is effective for government intelligence agencies in storing and categorizing the data sources. By effectively categorizing and storing the data, the data warehouse provides the analyst with a location where an effective query can produce tailored and specific results from vast stores of records. The data warehouse does this by linking the data sources through common threads. These threads are what allow the analyst to access the correct related information through the query. The data warehouse provides the structure of the data sources that the information will be categorized in. To be truly effective, a well-designed data...
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...CSCI 1507 (1903) "Enterprise level data work flows and Data Warehousing" Professor Rajni Palikhey University of Northern Virginia Acknowledgement This Research Paper would not have been possible without the guidance and the help of my co-students and respected Professor who in one way or the other contributed and extended their valuable assistance in the preparation and completion of this research paper. I would to like to convey my sense of gratitude to Professor.Rajni Palikhey who helped and supported us right throughout the semester. This paper would not have been possible without her cooperation and technical assistance. We would also thank our Institution and our faculty members without whom this project would have been a distant reality. We also extend our heartfelt thanks to our family and well wishers. I would like to take this occasion to specially thank University of Northern Virginia to provide us with excellent faculty and also in supporting us getting quality education remotely. Contents SL No Title Page no 1 Abstract 5 2 Introduction to Databases 6 3 OLTP and OLAP Systems 7 4 Difference between OLTP and OLAP 9 5 Data Modeling 13 6 Workflows in Enterprise level Data warehousing 18 7 Business Intelligence tools used in Data flow and Data Warehousing 21 8 Analysis in Data warehousing 24 9 Conclusion 28 10 Foot Note 30 11 References 31 ABSTRACT These days majority...
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