...Data Warehouses The basic reasons organizations implement data warehouses are: To perform server/disk bound tasks associated with querying and reporting on servers/disks not used by transaction processing systems most firms want to set up transaction processing systems so there is a high probability that transactions will be completed in what is judged to be an acceptable amount of time. Reports and queries, which can require a much greater range of limited server/disk resources than transaction processing, run on the servers/disks used by transaction processing systems can lower the probability that transactions complete in an acceptable amount of time. Or, running queries and reports, with their variable resource requirements, on the servers/disks used by transaction processing systems can make it quite complex to manage servers/disks so there is a high enough probability that acceptable response time can be achieved. Firms therefore may find that the least expensive and/or most organizationally expeditious way to obtain high probability of acceptable transaction processing response time is to implement a data warehousing architecture that uses separate servers/disks for some querying and reporting. To use data models and/or server technologies that speed up querying and reporting and that are not appropriate for transaction processing There are ways of modeling data that usually speed up querying and reporting (e.g., a star schema) and may not be appropriate for transaction...
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...Healthcare Data Warehousing Doug Kelley Health Informatics I Professor Lu December 7, 2012 Abstract ` Dimensional modeling lays the groundwork for data warehouses. Dimensional modeling is a similar process to traditional Entity/Relationship modeling in regards to tables (entities) having joins (relationships) with other tables via primary keys. Dimensional modeling has been used as a standard in industry for decision support systems in other areas such as transportation, production, sales and marketing. (Parmanto, 1) Because healthcare has many complex events, it has lagged behind other industries in terms of data warehousing. This paper will discuss several techniques that can help overcome these complexities. Introduction A data warehouse has been defined as a database optimized for long-term storage, retrieval, and analysis of records aggregated across patient populations, often serving the longer-term business and clinical analysis needs of an organization (Shortliffe, 932). For a data warehouse to perform these roles, it must be architected or modeled appropriately. There are a couple of different approaches to modeling data warehouses. Dimensional modeling is becoming standard approach. Background Review Designing a data warehouse for healthcare presents many unique challenges for designing a database. These include such complexities as multiple diagnoses, multiple payers, multiple physicians; primary and secondary, and late arriving data, such as...
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...Data Warehousing Saikrishna Burugula IST 7000 Data Management Wilmington University Abstract A data storage could be a subject-oriented, integrated, time-variant, non-updateable assortment of knowledge utilized in business intelligence and support of organizing decision-making method (Inmon, Strauss & Neushloss, 2008). In data warehousing when the data is stored it is not updated, commonly data warehousing intended for evaluation connected with data source in addition to addressing queries it can be called copy of addressing data (Prabhu, 2002). The key intention with this paper is typically to target on the actual design connected with data warehouse in addition to modeling techniques like ER modeling and Dimensional modeling. Introduction A Data Warehouse is not just a new combination of all of the in business databases in an organization. Because of its attention on business intelligence, exterior data, and time variant information, a data warehouse is usually a special type of database. The good thing is, you should not learn another number of database abilities to do business with a new information storage place. Most data warehouses tend to be relational databases designed in many ways optimized pertaining to selection assistance, definitely not in business information running. Facts warehousing could be the procedure whereby organizations create and gaze after information...
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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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...Importance of Data Warehousing Brenda L Bach The Digital Firm and Business Communications/BU 204-8A November 15, 2014 Ron Rosalik Kenneth and Jane Laudon state that a data warehouse is a database that stores current and historical data that can be of potential interest to decision makers throughout the corporation (Laudon, 2011. p.225). They go on to explain that the data can originate from many core operational transaction systems and could include data from Web site transactions (Laudon, 2011 p.225). Data warehouse extract current along with historical data from all operational systems within an organization. The data warehouse makes the data it collects and stores available to anyone and can be accessed and viewed as needed but cannot be altered in any manner. These data warehouses also provide a large range of ad hoc as well as analytical tools and graphical reports that represent the data. Companies often build enterprise data warehouses and either uses a central data warehouse or a smaller decentralized warehouse called a data mart to preserve the data they collect through its many sources. A data mart is a subset of a data warehouse that summarizes on a highly focused portion of the organization’s data and is placed within a separate data based for a very specific population of users. For example, a car dealer that deals with car sales as well as service may use a data mart to develop marketing and sales data that are specifically focused on the data of the customer...
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...Data warehousing: what is it, why do we want to do it, how is it done? How do data warehouses compare with operational databases? What do we want to consider when doing so, and what are our options in terms of implementation? In this article, I discuss data warehouses: what they are, how they compare to operational databases, and how they are designed, implemented and maintained. An operational database is one which is used by the enterprise to run its day to day operations. They are created to support fast transaction processing, with frequent updates. Speed is key to operational databases. They typically are used by clerical staff, and are on the order of megabytes of data to gigabytes. Database consistency is very important to operational databases, and consistency checks and constraints are rigidly enforced. They contain the most current set of data applicable to running enterprise operations. These are our sales and inventory databases. A data warehouse differs from this in many ways. They are used by management for making decisions, watching trends, and running reports. They are typically used offline, have few users and are very large: gigabytes to terabytes. They contain historical data, are read only, and are added to but rarely or never updated (the rows in the database are not changed, I mean). The data in the data warehouse is time sensitive – each row is the warehouse is timestamped so that trending of data versus time can be done. The kinds...
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...Training Curriculum Data Warehousing: • Introduction to Business Objects Enterprise Reporting • Fundamentals of Data warehouse Concepts • Introduction to Dimensional Modeling • Developing a Star Schema Reporting: • Building and editing queries with Web Intelligence • Performing on report analysis with Web Intelligence • Filtering Queries using conditions, prompts etc., • Using Combined Queries and merging dimensions • Displaying data in various formats (Ex: Tables, Charts etc.,) Advanced Reporting: • Calculations, Formulas and variables • Ranking Data, using Alerters to highlight data, Formatting numbers and Dates • Understanding Calculation Contexts • Web Intelligence Functions, Operators and Keywords • Calculating values with Smart Measures Universe Designer: • Designer and Universe Fundamentals • Creating a schema with Tables and Joins • Resolving Join problems in a schema • Defining Classes, Objects, hierarchies, using cascading list of values for hierarchies • Testing the universe • Working with OLAP universes Xcelsius 2008: • Application Overview • Creating and Updating Xcelsius visualizations • Using Xcelsius components ( Chart, Containers, Selectors etc.,) • Exporting Xcelsius visualizations to various applications (Power point, PDF, Flash • Creating templates, Alerts and Dynamic visibility • Using Data Manager ( Creating and configuring connections) • Live Office Connections, Query As A Web Service (QWAAS), XML data Connections Crystal...
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...Data warehousing is a fairly new but not so new development in the information systems field. Data warehousing can be traced back being in existence since the 1980’s when Teradata in 1983 introduced a database management system (DBMS) designed for decision support systems (Ponniah, 2010). The influence from the two Irish IBM architects Barry Devlin and Paul Murphy who in 1988 laid the foundations for what we call today a data warehouse in their original article “An Architecture for a Business and Information Systems” (Bouman & van Dongen, 2009). It was not long after that Bill Inmon, referred to as the father of data warehousing authored one of the most influential decision support books “Building the Data Warehouse” in 1991(Ponniah, 2010). Since then Prism Solutions in 1991 introduced the Prism Warehouse Manager software for developing data warehouses. The Data Warehousing Institute was also founded 1995 and has and continues to promote data warehousing by way of serving as the leading voice in the business world providing education, research, and support (Ponniah, 2010). With the business environment being more global, competitive, multifaceted and unpredictable there is a need for systems that would satisfy emerging business needs and new technological advances (Wixom & Watson, 2001). The enterprise for enhanced customer relations and e-commerce alone these days require large integrated repositories and advanced analytical repositories (Wixom & Watson, 2001). Data warehousing...
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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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...Term Paper Data Warehousing and Data Mining Term Paper Data Warehousing and Data Mining A Data Warehouse Saves Time, Enhances Data Quality and Consistency, Provides Historical Intelligence, Generates a High return on investment. One of the benefits to using a data warehouse is that it conveys Business Intelligence By providing data from various sources. Management executives no longer need to make decisions based on finite data. Time is saved by allowing business users to get data quickly from one location so they can make quick knowledgeable decisions and will not waste time trying to get data from several places. Using a data warehouse implementation provides for better data quality and consistency by converting data from different sources into an individual common format. Since data from each department is standardized, each department will have results that are in alignment with the results of all other departments. Doing so assures the accuracy of data and that provides for better business decisions. Since a data warehouse holds large volumes of historical data, it can analyze trends over a time frame so that predictions can be made. This type of data usually cannot be kept or used to get reports in a transactional database. Finally the return on investment for companies instituting a data warehouse and a Business Intelligence system generate more money while also saving money. The purpose of data mining is to provide knowledge to give company an advantage...
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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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...Implementation of a Data Warehouse Prototype for the University of Nairobi within the Context of Relational Online Analytical Processing (Data Analysis) PRESENTED BY: 1. JORAM KIPLIMO D61/68960/2013 0724431978 . PRESENTED TO: Dr. Muranga Njihia Word count: 2752 ABSTRACT Data ware housing is a booming industry with many interesting research problem. The data warehouse is concentrated on only few aspects. The discussion here is about the data warehouse design and usage in the case of the University of Nairobi Environment. Data warehouse can be built using a top-down approach, bottom – down approach or a combination of both. In this research paper we are discussing about the data warehouse design process. Data Warehouse (DWH) systems are used by decision makers for performance measurement and decision support. Currently the main focus of the DWH research field is not as much on the interaction of the DWH with the organization, its context and the way it supports the organization’s strategic goals, as on database issues. The aim of the study is to emphasize and describe the relationship between the DWH and the organization with conceptual models, and to use this knowledge to support data interpretation with business metadata. KEYWORDS Data Warehouse (DWH) Data Mart Extraction, Transformation and Loading (ETL); Software that is used to extract data from a data source like a operational system or data warehouse, modify the data and then...
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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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...head: DATABASE AND DATA WAREHOUSING DESIGN Database and Data Warehousing Design Yolanda McCollum Dr. Constance D. Blanson CIS499: Information Systems Capstone February 11, 2014 Database and Data Warehousing Design Why does one need data warehousing? According to Total Metrics (2013): 1. The prime purpose of a Data Warehouse is to store, in one system, data and information that originates from multiple applications within, or across, organizations. The data may be stored ‘as received’ from the source application, or it may be processed upon input to validate, translate, aggregate, or derive new data/information. 2. Most of the data load functions are processed in batch. There are few on-line data maintenance functions. The on-line functions that do exist tend to update the reference files and data translation tables. 3. A database alone does not constitute a Data Warehouse system. At a minimum, a Data Warehouse system must include the database and corresponding load functions. Data reporting functions are optional. They may, or may not be an integral part of the Data Warehouse system. 4. The prime purpose of storing the data is to support the information reporting requirements of an organization i.e. multiple users and multiple applications. 5. The Data Warehouse may, or may not, provide the required reporting functions. In some cases, external applications access the Data Warehouse files to generate their own reports and queries. 6. Data Warehouse functions...
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