Data mart

About: Data mart is a research topic. Over the lifetime, 559 publications have been published within this topic receiving 8550 citations.

A Conceptual Modeling Approach for OLAP Personalization

Abstract: Data warehouses rely on multidimensional models in order to provide decision makers with appropriate structures to intuitively analyze data with OLAP technologies However, data warehouses may be potentially large and multidimensional structures become increasingly complex to be understood at a glance Even if a departmental data warehouse (also known as data mart) is used, these structures would be also too complex As a consequence, acquiring the required information is more costly than expected and decision makers using OLAP tools may get frustrated In this context, current approaches for data warehouse design are focused on deriving a unique OLAP schema for all analysts from their previously stated information requirements, which is not enough to lighten the complexity of the decision making process To overcome this drawback, we argue for personalizing multidimensional models for OLAP technologies according to the continuously changing user characteristics, context, requirements and behaviour In this paper, we present a novel approach to personalizing OLAP systems at the conceptual level based on the underlying multidimensional model of the data warehouse, a user model and a set of personalization rules The great advantage of our approach is that a personalized OLAP schema is provided for each decision maker contributing to better satisfy their specific analysis needs Finally, we show the applicability of our approach through a sample scenario based on our CASE tool for data warehouse development

Business Analytics in (a) Blink

Abstract: The Blink project’s ambitious goal is to answer all Business Intelligence (BI) queries in mere seconds, regardless of the database size, with an extremely low total cost of ownership. Blink is a new DBMS aimed primarily at read-mostly BI query processing that exploits scale-out of commodity multi-core processors and cheap DRAM to retain a (copy of a) data mart completely in main memory. Additionally, it exploits proprietary compression technology and cache-conscious algorithms that reduce memory bandwidth consumption and allow most SQL query processing to be performed on the compressed data. Blink always scans (portions of) the data mart in parallel on all nodes, without using any indexes or materialized views, and without any query optimizer to choose among them. The Blink technology has thus far been incorporated into two IBM accelerator products generally available since March 2011. We are now working on the next generation of Blink, which will significantly expand the “sweet spot” of the Blink technology to much larger, disk-based warehouses and allow Blink to “own” the data, rather than copies of it.

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

A Requirement-driven Approach for Designing Data Warehouses

Abstract: . A data warehouse (DW) is an integrated and historised collection of data generally used to make strategic decisions by means of online analytical processing techniques. Most of the existing DW evelopment tools used nowadays in the industry focuses on the structures for data storage, e.g. applying the star or snowflake schema. We believe that DW that better suit the needs of decision makers would be delivered by concentrating more on their requirements. So far, very few approaches have been proposed to elicit DW requirements. This paper proposes a method, called CADWA, for guiding DW analyst in the elicitation of decisions-makers requirements and in their operationalization into a DW model. CADWA shifts the focus from where information from on how they should be structured and why they are needed. To comply with current practice, the approach starts with the elicitation of highlevel requirements, reuses a set of data mart (DM) models and produces a model for the new DW. The paper presents each stage of the CADWA approach, and provides illustrations with an example inspired from a real case.

Efficient execution of joins in a star schema

Abstract: A star schema is very popular for modeling data warehouses and data marts. Therefore, it is important that a database system which is used for implementing such a data warehouse or data mart is able to efficiently handle operations on such a schema. In this paper we will describe how one of these operations, the join operation --- probably the most important operation --- is implemented in the IBM Informix Extended Parallel Server (XPS).