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| Content Provider | ACM Digital Library |
|---|---|
| Copyright Year | 2001 |
| Abstract | Data warehouses are databases devoted to analytical processing. They are used to support decision-making activities in most modern business settings, when complex data sets have to be studied and analyzed. The technology for analytical processing assumes that data are presented in the form of simple data marts, consisting of a well-identified collection of facts and data analysis dimensions (star schema). Despite the wide diffusion of data warehouse technology and concepts, we still miss methods that help and guide the designer in identifying and extracting such data marts out of an enterprisewide information system, covering the upstream, requirement-driven stages of the design process. Many existing methods and tools support the activities related to the efficient implementation of data marts on top of specialized technology (such as the ROLAP or MOLAP data servers). This paper presents a method to support the identification and design of data marts. The method is based on three basic steps. A first top-down step makes it possible to elicit and consolidate user requirements and expectations. This is accomplished by exploiting a goal-oriented process based on the Goal/Question/Metric paradigm developed at the University of Maryland. Ideal data marts are derived from user requirements. The second bottom-up step extracts candidate data marts |
| Starting Page | 452 |
| Ending Page | 483 |
| Page Count | 32 |
| File Format | |
| ISSN | 1049331X |
| e-ISSN | 15577392 |
| DOI | 10.1145/384189.384190 |
| Volume Number | 10 |
| Issue Number | 4 |
| Journal | ACM Transactions on Software Engineering and Methodology (TOSEM) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2001-10-01 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Conceptual modeling Data mart Data warehouse Design method Software quality management |
| Content Type | Text |
| Resource Type | Article |
| Subject | Software |
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