...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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...Running 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...
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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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...DATA WAREHOUSE A data warehouse is a logical collection of information gathered from different operational databases and used to create business intelligence that supports decision making tasks and business analysis activities. Walmart is known for having the biggest data warehouse used, which is larger then 4 Petabytes. Wal-Mart is very secretive about their data warehouse. After achieving a major milestone Teradata, Wal-Mart’s data warehouse supplier, was given permission to announce a few shallow facts about Wal-Mart’s data warehouse. • Wal-Mart has indeed the world’s largest, non military, database with a size of one-half a petabyte. • It is the world’s largest data warehouse Wal-Mart keeps track of 100 million customers buying billions of products every week. Using this data allows Wal-Mart to achieve Always Low Prices. It is the data warehouse that enabled Wal-Mart to become one of the 15 most profitable companies in the world. Let’s look at some sales questions Wal-Mart’s data warehouse has to answer: • How much orange juice did we sell last year, last month, last week in store X? • Comparing sales data of orange juice in various stores? • What internal factors (position in store, advertising campaigns...) in- fluence orange juice sales? • What external factors (weather...) influence orange juice sales? • Who bought orange juice last year, last month, last week? A data warehouse really is a Decision Support System (DSS). Providing the data to support business decisions...
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...(a) What is Data Mining? What is OLAP? How is data mining different from OLAP? Data mining: Data mining is essential utilized today by organizations with an in number buyer center retail, budgetary, correspondence, and promoting associations. It empowers these organizations to figure out relationships around "interior" components, for example, value, item positioning, or staff abilities, and "outer" variables, for example, financial pointers, rivalry, and client demographics. Also, it empowers them to figure out the effect on deals, client fulfillment, and corporate benefits. At long last, it empowers them to "penetrate down" into synopsis data to view part transactional information. OLAP: OLAP stays for Online Analytical Processing and is designing used to accumulate, regulate and process multidimensional data and outfit brisk access to this data for demonstrative purposes. OLAP is for the most part used inside business reporting for promoting, deals, human possession organization and diverse business fields. OLAP mulls over brisk execution of complex database requests continuously. OLAP energizes complex data sees through data turning, complex data transforming, and data showing. OLAP manages dimensional information, which takes into account much quicker execution of complex database inquiries contrasted with social database administration frameworks. The information structure that OLAP make from the social information is called OLAP block. OLAP solid shapes might be...
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...Data Mining In today’s society there is more data and information that is being stored it is hard to sort through it all and pick out useful information. All of this information is being stored in data warehouses which are basically a huge database filled with information collected over many years. This is why data mining is so popular, it has the ability to search through these huge amounts of data and find useful information, or will be able to see patterns in the information that may have not been noticed any other way. Also data mining tools can help organize data to clarify any patterns found, this allows for more accurate and reliable findings. There are many different types of patterns that can be found with data mining that could be useful to stores, such as what products are often sold together or what products are sold best in cretin areas. There are some different tools used when data mining some examples would be classification tools, clustering analysis tools and association rules discovery. Before these tools for data mining were used the way people used to find information was with hypothesis verification, this technique would find information based on what the user’s hypothesis which limited how effective this tool was (Ramsey). Classification tools are the most commonly used in data mining, these tools have the ability to try and difference between objects or actions. An example of this would be if a company wants to know what part of their advertisements...
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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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...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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...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 MINING AND DATA WAEHOUSING POSSIBLE OBJECTIVE TYPE QUESTIONS & ANSWERS 1. Data scrubbing is the A. process of rejecting the data in the data warehouse and to create the necessary indexes B. process of accepting the data from the data warehouse and to create the necessary indexes C. A process to upgrade the quality of data before it is moved into a data warehouse D. A process of upgrading the quality of data after it is moved into a data warehouse ANSWER : C 2. The architecture of Active data warehouse includes which of the following? A. Closer to real-time updates B. As a minimum of one data mart C. Data that can extracted from numerous internal and external sources D. All of the above. ANSWER : D 3. An operational system which means...
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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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...Project on Data Management Submitted By: Name……………………. Section………………….. Question1. What are the components of quality data? Answer1. The latest information technology dimensions has enabled to make diverse use of data and turning the raw data into meaningful information to extract the quality. Data can be referred to raw numbers, figures which are useless unless they are put into a form and converted into a useful information. A data is said to be effective when it is converted into useful form or rather can be used to provide some information. When the raw data which is numeric, figures is converted into meaningful information, it is said to be known as information which can be used to give the data a meaning. Data quality is defined by its usefulness. When the data offers accurate information regarding a person or an organization then it is said to be known as data quality. The components of data quality can be analyzed with the help of the following data elements: 1. Accuracy which defines how correct and precise is the data 2. Completeness, the comprehensiveness of data also defines the data quality 3. Timeliness, the timely updates on the data also ensures that the data is correct and free from any discrepancy 4. Relevancy, the data that is gathered should be relevant which defines the purpose and fulfills the use. It defines that the data gathered accomplish the purpose 5. The last element of data quality is its availability. It defines the availability...
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...A data warehouse is a subject-oriented, integrated, time-variant, nonupdateable collection of data used to support management decision-making and business intelligence (Hoffer, 2011). Business Intelligence (BI) is a term that describes a comprehensive, cohesive, and integrated set of tools and processes used to capture, collect, integrate, store and analyze data with the purpose of generating and presenting information to support decision making (Coronel, Morris, & Rob, 2013). Data Warehouse A data warehouse enables an organization to obtain the information about future trends and track customer demands. The key terms that define data warehouse are subject-oriented, integrated, time-variant, and nonupdateable. Each one has its meaning and importance in data warehousing. Subject-oriented – A data warehouse is organized around the key subjects that may include but not limited to customers, patients, students, products, and time. Integrated – The data in the data warehouse are defined using consistent naming conventions, formats, structures, and related characteristics. This means data warehouse holds one version of “the truth”. Time-variant – Data in the data warehouse contain a time dimension so they could be used to study trends and changes. Nonupdateable – Once the data gets loaded into the data warehouse, it could not be updated by the end users. Data warehousing is a process where organizations create and maintain data warehouses and extract necessary...
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...computing, 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:...
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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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