Showing papers on "Data mart published in 2000"

From enterprise models to dimensional models: a methodology for data warehouse and data mart design.

Abstract: This paper describes a method for developing dimensional models from traditional Entity Relationship models. This can be used to design data warehouses and data marts based on enterprise data models. The first step of the method involves classifying entities in the data model into a number of categories. The second step involves identifying hierarchies that exist in the model. The final step involves collapsing these hierarchies and aggregating transaction data to form dimensional models. A number of design alternatives are presented, including a flat schema, a terraced schema, a star schema and a snowflake schema. We also define a new type of schema called a star cluster schema. This is a restricted form of snowflake schema, which minimises the number of tables while avoiding overlap between different dimensional hierarchies. Individual schemas can be collected together to form constellations or galaxies. We illustrate the method using a simple example.

System and method for managing data privacy in a database management system including a dependently connected privacy data mart

Abstract: A system for managing data privacy comprises a database management system for storing data from a plurality of consumer database tables, with irrevocable logging of all access, whether granted or denied, to the data contents stored in the consumer data tables; a privacy metadata system that administers and records all data, users and usage of data that is registered as containing privacy elements; and a replication system that feeds the consumer access system with personal consumer data, maintains integrity of the consumer data and provides changes and corrections back to the originating database management system through their own integrity filters as well as a means of storage and the mechanism to provide input for changes in the personal data or privacy preferences. The system further includes means for managing consumer notification, access, correction and change of preferences for privacy or data protection in the privacy metadata system.

e-Data: Turning Data Into Information With Data Warehousing

Abstract: Foreword. Acknowledgments. About the Author. Introduction. The Book and Its Purpose. You the Reader. Content Overview. Part I: Getting the Value. Part II: Getting the Technology. Part III: Getting Ready. A Case Study Sneak Preview. Requisite Caveats. I. GETTING THE VALUE. 1. What Is a Data Warehouse Anyway? The Data Warehouse Defined. Data Warehousing, Decision Support, and Business Intelligence. The Data-Warehousing Bandwagon and Why Everyone Jumped on It. Data-Warehousing Objectives. Some Trite Data-Warehousing Aphorisms. Venus and Mars: How IT and Businesspeople Communicate. Some Other Buzzwords and What They Mean. Some Lingering Questions. 2. Decision Support from the Bottom Up. The Evolution of Decision Support. Standard Query: The Workhorse of DSS. Multidimensional Analysis: The Power of Slice 'n' Dice. Modeling and Segmentation: Analysis for Knowledge Workers. Knowledge Discovery: The Power of the Unknown. Some Real-Life Examples. Standard Queries. Multidimensional Analysis. Modeling and Segmentation. Knowledge Discovery. Wherefore Data Mining? Data Warehousing in the Real World. What It Takes to Get to the Top. 3. Data Warehouses and Database Marketing. Customer Relationship Management. Customer Segmentation. Individual Customer Analysis. Case Study: Bank of America. A Word about CRM Technology. Popular Database-Marketing Initiatives and What They Mean. Target Marketing. Cross-Selling. Sales Analysis and Forecasting. Market Basket Analysis. Promotions Analysis. Customer Retention and Churn Analysis. Profitability Analysis. Customer Value Measurement. Product Packaging. Call Centers. Sales Contract Analysis. Database Marketing Lessons Learned. Some Lingering Questions. 4. Data Warehousing by Industry. Retail. Uses of Data Warehousing in Retail. Market Basket Analysis. In-Store Product Placement. Product Pricing. Product Movement and the Supply Chain. The Good News and Bad News in Retailing. Case Study: Hallmark. Financial Services. Uses of Data Warehousing in Financial Services. The Good News and Bad News in Financial Services. Case Study: Royal Bank of Canada. Telecommunications. U.S. Local Service Carriers. U.S. Long-Distance Carriers. International Long-Distance Carriers. Wireless Carriers. Uses of Data Warehousing in Telecommunications. The Good News and Bad News in Telecommunications. Case Study: GTE. Transportation. Yield Management. Frequent-Passenger Programs. Travel Packaging and Pricing. Fuel Management. Customer Retention. The Good News and Bad News in Transportation. Case Study: Qantas. Government. The Good News and Bad News in Government. Case Study: State of Michigan. Health Care. Uses of Data Warehousing in Health Care. The Good News and Bad News in Health Care. Case Study: Aetna U.S. Healthcare, U.S. Quality Algorithms. Insurance. Uses of Data Warehousing in Insurance. The Good News and Bad News in the Insurance Industry. Case Study: California State Automobile Association. Entertainment. Case Study: Twentieth Century Fox. Some Lingering Questions. II. GETTING THE TECHNOLOGY. 5. The Underlying Technologies: A Primer. Data Warehouse Architecture. The Operational Data Store. Two-Tier Versus n-Tier. Middleware. Databases and What They're Good For. Multidimensional Databases. Metadata. Disseminating the Information: Application Software. Graphical User Interfaces. A Word about the Web. Development Definitions and Differentiators. OLAP Subcategories. Data Modeling and Design Tools. Data Extraction and Loading Tools. Management and Administration. Putting It All Together. Some Lingering Questions. 6. What Managers Should Know about Implementation. What You Should Know about Data Warehouse Methodologies. Evaluating a Methodology. The Data Warehouse Implementation Process. The Steps in Data Structure and Management. The Steps in Application Development. Who Should Be Doing What? Development Job Roles and Responsibilities. Consultants Versus Full-Time Staff. The Lost Fine Art of Skill Delineation. Good and Evil Square Off:A Tale of Two Project Plans. Executive Involvement on the Project. Profile: Hank Steermann of Sears, Roebuck and Co. Some Lingering Questions. 7. Value or Vapor? Finding the Right Vendors. The Hardware Vendors. Five Questions to Ask Your Hardware Vendor. The Database Vendors. Five Questions to Ask Your Database Vendor. TPC Benchmarks. The Application Vendors. Five Questions to Ask Your Application Tool Vendor. Data-Mining Tools: A Breed Apart. Ten Questions to Ask Your Data-Mining Vendor. The Consultants. The Big Guys. The Little Guys. A Word about the Analysts. A Word about the Vendors. Five Questions Your Consultant Should Ask You. The RFP Process. The Components of a Good RFP. A Sample Table of Contents. Some Lingering Questions. III. GETTING READY. 8. Data Warehousing's Business Value Proposition. Return on Investment. Hard ROI: The Tangible Benefits. Soft ROI: The Intangible Benefits. Budgeting for the Data Warehouse. Technology Costing. Resource Costing. Obtaining Funding - But Not Too Much! Data Warehouse Operations Planning. Developing an Operating Plan. Are You Ready for a Data Warehouse? A Quiz. Data Warehouse Readiness Score. Some Lingering Questions. 9. The Perils and Pitfalls. The New Top 10 Data-Warehousing Pitfalls. Pitfall #1: The Data Warehouse as Panacea Syndrome. Pitfall #2: They Talked to End-Users--But the Wrong Ones! Pitfall #3: Too Much Time Spent on Research, Alienating Constituents. Pitfall #4: Bogging a Good Project Down by Creating Metadata. Pitfall #5: Being Sidetracked by "Neat to Know" Analysis. Pitfall #6: Adopting Decision Support Without Supporting Decisions. Pitfall #7: Greediness on the Part of Development Organizations. Pitfall #8: Lack of "Internal PR". Pitfall #9: Failing to Acknowledge That DSS Applications Are Finite. Pitfall #10: Overemphasizing Development and Ignoring Deployment. Thinking of Outsourcing? Data Warehousing's Dirty Little Secrets. The Politics of Data Warehousing. The Top 10 Signs of Data Warehouse Sabotage. The Vanguards of Data Warehousing. Case Study: Charles Schwab & Co., Inc. 10. What to Do Now. If You Need a Data Warehouse. Establish Up-Front Success Metrics. Consider Benchmarking. Research External Staff. Prepare Your Environment. Classify Your Stakeholders. Ramp Up Support Capabilities. Profile: Philippe Klee, Qantas Airways. Look Outside Your Box. Solicit a Request for Information. If You Already Have a Data Warehouse. Establish a Formal Postmortem Process. Inventory Existing Applications. Spring for an Audit. Improve Customer-Facing Business Processes. Establish a Closed-Loop Process. Go Web, Young Man! Case Study: Allsport. Consider Branching Out Vertically. Consider Branching Out Horizontally. If You Have a Data Mart or Marketing Analysis System. Share Your Toys. Migrate to Enterprisewide. An Insider's Crystal Ball. Clickstream Storage. Enterprise Resource Planning. Extending the Data Warehouse to External Vendors. Customized Web Portals. Real-Time E-Marketing. Privacy. The Whole Truth. Appendix: Haven't Had Enough? Suggested Reading. Business Books. Technology Books. Websites. Index. 0201657805T04062001

Method and apparatus for processing business information from multiple enterprises

Abstract: A method for processing business information generated by multiple enterprises is provided. Initially, a data warehouse is provided that has the capability of holding business information. The data warehouse is populated with business information received from a first enterprise and a second enterprise. The business information is associated with the first and second enterprise based upon a set of standardized categories used in the data warehouse. Metastore information describing the organization of business information in the data warehouse is used to develop rules for extracting a portion of the business information from the data warehouse. This portion of business information extracted from the data warehouse is then stored in the data mart. Using at least one such data mart, a multiple dimension database is created wherein each dimension of the multiple dimension database corresponds to variables derived from business rules established in an industry. Using the multiple dimension database, metrics for measuring business performance from the multiple dimension databases are then generated.

Data Warehouse Refreshment

Abstract: The central problem addressed in this chapter is the refreshment of a data warehouse in order to reflect the changes that have occurred in the sources from which the data warehouse is defined. The possibility of having “fresh data” in a warehouse, is a key factor for success in business applications. In many activities, such as in retailing, business applications rely on the proper refreshment of their warehouses. For instance, [Jahn96] mentions the case of WalMart, the world’s most successful retailer. Many of WalMart’s large volume suppliers, such as Procter & Gamble, have direct access to the WalMart data warehouse, so they deliver goods to specific stores as needed. WalMart pays such companies for their products only when it is sold. Procter & Gamble ships 40% of its items in this way, eliminating paperwork and sale calls on both sides. It is essential for the supplier to use fresh data in order to establish accurate shipment plans and to know how much money is due from the retailer. Another example is Casino Supermarche, in France, which recouped several millions dollars when they noticed that Coca-Cola was often out of stock in many of their stores. Freshness of data does not necessarily refer to the highest currency but the currency required by the users. Clearly, applications have different requirements with respect to the freshness of data.

Hierarchically distributed data warehouse

Abstract: Data warehouses (DW) are a rapidly developing field of both application and research. Up to now, three types of data warehouse have been proposed, which include centralized DW, data mart, and distributed DW. In this paper, a new type of data warehouse, called hierarchically distributed data warehouse (HDDW), is developed on the basis of a DW building case. HDDW-oriented OLAP is also studied and a C/S architecture for OLAP of HDDW is given.

Method for dynamic customer profiling in database

Abstract: According to the present invention creating and accessing customer information (402) in a database, data mart or data warehouse, segmenting customer information into one or more groups of customers (404), having one or more attributes. The segmenting can be based upon a value for the attribute of interest. Defining one or more profiles (406) are part of the method, a profile can comprise one or more groups, analyzing customer information (408) for a plurality of users, evaluating to specific values used to select which group a particular customer belongs, mapping to specific customer groups based on segment code values assigned to the various groups of customers.

Metadata for Data Warehouse

Abstract: Metadata is the foundation for success of Data warehouse. Metadata is central piece of the whole Data Warehousing Concepts. Metadata allows the end user to be pro-active in the use of the warehouse. It is the Information Directory containing Yellow Pages, Road Map and ‘Places of Interest’ for navigating the warehouse.

Data Warehouse Strategy to Enable Performance Analysis

Abstract: The Virginia Department of Transportation (VDOT) has engaged to implement an enterprise data warehouse as part of a strategic investment in its information technology (IT) infrastructure. Data warehousing provides an information architecture that serves as the enterprisewide source of data for performance analysis and organizational reporting. To assist VDOT in achieving its strategic outcome area objectives, a programming and scheduling (P&S) data mart is being developed to track preconstruction project activities. This data mart and subsequent data marts function as departmental decision support platforms, enabling VDOT's operating divisions to perform their own enhanced analytical processing, visualization, and data mining for more informed business decision capabilities. Presented is a case study based on the enterprise data warehouse and P&S data mart being developed and implemented for VDOT by TransCore. Explicitly described is how one VDOT division, Programming and Scheduling, will benefit by inves...

Data Warehouse Practice: An Overview

Abstract: Since the beginning of data warehousing in the early 1990s, an informal consensus has been reached concerning the major terms and components involved in data warehousing. In this chapter, we first explain the main terms and components. Data warehouse vendors are pursuing different strategies in supporting this basic framework. We review a few of the major product families and show in the next chapter a brief survey of the basic problem areas data warehouse practice and research is faced with today. These issues are then treated in more depth in the remainder of this book.

Deploying the business case to reduce the risk of large scale data warehousing

Abstract: Managers of large data warehousing projects often put project failure down to organizational resistance. Technical requirements are usually not considered to be crucial for project success. However, the majority of the scientific work on data warehousing concentrates on technical aspects. As a consequence, a comprehensive framework or method for the introduction of a data warehouse is still missing. This paper takes this contradiction into account and deals with the management of organizational risks in large scale data warehouse projects. We base our research on information gathered from large organizations which develop and/or run data warehouses.This paper is structured as follows: After a short introduction the planning process of data warehousing is outlined. The first step is the strategic decision for data warehousing which is followed by the definition and evaluation of the initial project which is also called first increment. In the third section we present a business case strategy for both the initial project and subsequent data mart projects. Furthermore, the interdependencies between the initial phase and subsequent projects are discussed.

Data warehouse research - Issues and projects

Abstract: In the previous chapter, we have given a broad-brush state of the practice in data warehousing. In this chapter, we look at more or less the same issues again, focusing, however, on problems rather than solutions. Each of the topics we address is covered in the following chapters. In Section 2.6, we briefly review some larger research projects which address more than one of the issues and will therefore be cited in several places throughout the book. Finally, Section 2.7 takes a critical overall look at this work and introduces the DWQ conceptual framework which takes the business perspective of data warehousing into account as well as the so far dominant technical aspects.

Data Marts: Key to Reviving the Enterprise Data Warehouse

Abstract: This article discusses how the use of data marts can help you to implement the traditional enterprise data warehouse.

An information strategy to enable the analysis of performance measures

Abstract: To assist VDOT in achieving its strategic outcome area objectives, a Programming and Scheduling (P&S) Data Mart is also being developed to track pre-construction project activities. This data mart and subsequent data marts function as departmental decision support platforms enabling VDOTs operating divisions to perform their own enhanced analytical processing, visualization, and data mining for more informed business decisions capabilities. This paper presents a case study based on the enterprise data warehouse and P&S Data Mart being developed and implemented for VDOT and it describes how the P&S Division in VDOT will benefit by investing in IT to achieve its strategic goals

Translating Advances in Data Mining in Business Operations: The Art of Data Mining in Retailing

Abstract: Knowledge discovery in databases (KDD) is a field of research that studies the development and use of various data analysis tools and techniques. KDD research has produced an array of models, theories, functions and methodologies for producing knowledge from data. However, despite these advances, nearly two thirds of information technology (IT) managers say that data mining products are too difficult to use in a business context. This chapter discusses how advances in data mining translate into the business context. It highlights the art of business implementation rather than the science of KDD. INTRODUCTION In the past, high storage and processing costs meant that businesses had to be selective about what data they stored. Today, this restriction has been removed as costs of data storage plummet. In addition, there are now more opportunities for capturing detailed data, particularly with the increase in e-commerce activities, where detailed 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com IDEA GROUP PUBLISHING This chapter appears in the book, Organizational Data Mining: Leveraging Enterprise Data Resources for Optimal Performance, edited by Hamid R. Nemati and Christopher D. Barko. Copyright © 2004, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.

The Research of Developing Methods of Data Warehouse Based on Internet/Intranet

Abstract: Data warehouse technology is new IT technology that engenders in 90s. Today,information systems are generally based on Internet/Intranet. So data warehouses founded on MIS are also based on Internet/Intranet. According to planing and designing a number of data warehouse cases,the author researches the developing methods of data warehouse of information system based oon Internet/Intranet and gives the proposal of developing or realizing the applications of information system based on Internet/Intranet like on line analysis process,decision support and others.

QFD Matrix for Incremental Construction of a Warehouse via Data Marts

Abstract: In this paper we consider the construction of a dimensional data warehouse. The warehouse is built beginning with the first data mart and proceeding in an iterative manner constructing one mart at a time. In this way the warehouse is seen to evolve over time. This evolutionary process is necessary due to the complexity of data stores, relationships, transformations, and the processing involved. In this paper we consider the problem of identifying the next data mart to construct and present a tool based on Quality Function Deployment for use in the planning stages.

Bringing the data mart into the curriculum

Abstract: The development and deployment of data warehouses have become critical activities in strategic planning and decision-making. Computer and information science students should have an early hands-on exposure to the theoretical and practical aspects of this complex technology. This paper describes the first steps in an ambitious program for bringing the data warehouse into the curriculum. With the support of a NSF-ILI grant, two teams of senior students designed and developed a large-scale data repository on an Oracle platform as part of their capstone projects. This paper reports on the early phase of that process during which students established a data mart with drill-down and advanced data visualization capabilities. The impact of the project on current and future activities in the lab and in the classroom is discussed.

Modeling of an Object-Oriented Data Mart for Knowledge Discovery in Multimedia Metadata.

Abstract: This paper describes the modeling of a data mart designed for use as a data repository in the “knowledge discovery in data bases” (KDD) process applied to a multimedia metadata environment whose data structure is based on the MHEG-5 standard. The data mart design uses the features of the original repository which are meaningful for the KDD process. The typical data warehouse structure was adapted to the object-oriented multimedia environment, and a structure that is both homogeneous – capable of integrating information from different sources – and objective – providing specific content and functionality for analysis – was generated to support the data preparation process before the data mining operation.

Criando data warehouse com o modelo dimensional

Abstract: Stored knowledge in organizational databases during the years is vital for the survival of a company in a competitive business world. For its effective use as support in a decision-making process of a organization, data must be organized for fast access and easy comprehension. A dimensional modeling is provide as an alternative for the construction of a data warehouse, with its elements, modeling techniques, and steps for its development to meet the mentioned requirements. Results and considerations about a case study are given, together with the application of a dimensional model for the creation of a data mart, for a juridical internet portal