Data mart

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

90 days to the data mart

Abstract: From concept to deployment in just 13 weeks! A proven approach to building a fully functional data mart quickly, efficiently, inexpensively. From one of the leading data warehousing experts, here is a proven approach to building a departmental data mart in 13 weeks. Rather than provide a cookbook describing how to build a particular type of data mart using a specific set of tools, bestselling author Alan Simon provides you with a detailed blueprint that can be used to construct virtually any type of data mart. Following a brief overview of key concepts and terms, Simon launches into a week-by-week action plan, beginning with the initial planning and project definition stages in Weeks 1 and 2, and ending with acceptance testing, fine-tuning, and deployment in Week 13. The approach outlined works equally well for both ROLAP and MOLAP data marts, regardless of the products or technologies you use to build your mart. Whether your goal is to provide basic querying reporting, OLAP, EIS, data mining, or any combination of these, you'll find everything you need to get the job done right, in record time, and within budget, including: A proven approach to building a data mart in 90 days Step-by-step guidelines to all organizational, procedural, and technical steps involved Numerous checklists that help you make sure all the bases are covered Expert advice on how to make the most of particular technologies and products. VISIT OUR WEBSITE AT www wiley.com/compbooks/

Performing a data analysis process

Abstract: A data analysis workbench enables a user to define a data analysis process that includes an extract sub-process to obtain transactional data from a source system, a load sub-process for providing the extracted data to a data warehouse or data mart, a data mining analysis sub-process to use the obtained transactional data, and a deployment sub-process to make the data mining results accessible by another computer program Common settings used by each of the sub-processes are defined, as are specialized settings relevant to each of the sub-processes The invention also enables a user to define an order in which the defined sub-processes are to be executed The defined data analysis process then is able to be performed by one or more computer systems

Agile Data Warehousing Project Management: Business Intelligence Systems Using Scrum

Abstract: You have to make sense of enormous amounts of data, and while the notion of "agile data warehousing" might sound tricky, it can yield as much as a 3-to-1 speed advantage while cutting project costs in half. Bring this highly effective technique to your organization with the wisdom of agile data warehousing expert Ralph Hughes. Agile Data Warehousing Project Management will give you a thorough introduction to the method as you would practice it in the project room to build a serious "data mart." Regardless of where you are today, this step-by-step implementation guide will prepare you to join or even lead a team in visualizing, building, and validating a single component to an enterprise data warehouse. * Provides a thorough grounding on the mechanics of Scrum as well as practical advice on keeping your team on track * Includes strategies for getting accurate and actionable requirements from a team's business partner * Revolutionary estimating techniques that make forecasting labor far more understandable and accurate * Demonstrates a blends of Agile methods to simplify team management and synchronize inputs across IT specialties * Enables you and your teams to start simple and progress steadily to world-class performance levels Table of Contents Part I: A Generic Agile Method Chapter 1. Why Agile? Chapter 2. Agile Development in a Nutshell Chapter 3. Project Management Lite Chapter 4. User Stories for Business Intelligence Applications Part II. Adapting Agile to Data Warehousing Chapter 5. Developer Stories for Data Integration Projects Chapter 6. Agile Estimation for DW/BI Chapter 7. Further Adaptations for Agile Data Warehousing Chapter 8. Starting and Scaling Agile Warehousing Teams Part III. Retrospective Chapter 9. Faster, Better, Cheaper

Towards an automatic data mart design

Abstract: The Data Warehouse design involves the definition of structures that enable an efficient access to information. The designer builds a multidimensional structure taking into account the users requirements. In fact, it is a highly complex engineering task that calls for a methodological support. This paper lays the grounds for an automatic, stepwise approach for the generation of data warehouse and data mart schemes. For this, it first proposes a standard format for OLAP requirement acquisition. Secondly, it defines an algorithm that transforms automatically the OLAP requirements into data marts modelled either as star or constellation schemes. Thirdly, it overviews our mapping rules between the data sources and the data marts schemes.

Statistical Sampling to Instantiate Materialized View Selection Problems in Data Warehouses

Abstract: In any online decision support system, the backbone is a data warehouse. In order to facilitate rapid response to complex business decision support queries, it is a common practice to materialize an appropriate set of the views at the data warehouse. However, it typically requires the solution of the Materialized View Selection (MVS) problem to select the right set of views to materialize in order to achieve a certain level of service given a limited amount of resource such as materialization time, storage space, or view maintenance time. Dynamic changes in the source data and the end users requirement necessitate rapid and repetitive instantiation and solution of the MVS problem. In an online decision support context, time is of the essence in finding acceptable solutions to this problem. In this chapter, we have used a novel approach to instantiate and solve four versions of the MVS problem using three sampling techniques and two databases. We compared these solutions with the optimal solutions corresponding to the actual problems. In our experimentation, we found that the sampling approach resulted in substantial savings in time while producing good solutions.