The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A General Model for Data Warehouses
Abstract
Basically, the schema of a data warehouse lies on two kinds of elements: facts and dimensions. Facts are used to memorize measures about situations or events. Dimensions are used to analyse these measures, particularly through aggregation operations (counting, summation, average, etc.). To fix the ideas let us consider the analysis of the sales in a shop according to the product type and to the month in the year. Each sale of a product is a fact. One can characterize it by a quantity. One can calculate an aggregation function on the quantities of several facts. For example, one can make the sum of quantities sold for the product type “mineral water” during January in 2001, 2002 and 2003. Product type is a criterion of the dimension Product. Month and Year are criteria of the dimension Time. A quantity is so connected both with a type of product and with a month of one year. This type of connection concerns the organization of facts with regard to dimensions. On the other hand a month is connected to one year. This type of connection concerns the organization of criteria within a dimension. The possibilities of fact analysis depend on these two forms of connection and on the schema of the warehouse. This schema is chosen by the designer in accordance with the users needs. Determining the schema of a data warehouse cannot be achieved without adequate modelling of dimensions and facts. In this article we present a general model for dimensions and facts and their relationships. This model will facilitate greatly the choice of the schema and its manipulation by the users.
Related Content
|
Girija Ramdas, Irfan Naufal Umar, Nurullizam Jamiat, Nurul Azni Mhd Alkasirah.
© 2024.
18 pages.
|
|
Natalia Riapina.
© 2024.
29 pages.
|
|
Xinyu Chen, Wan Ahmad Jaafar Wan Yahaya.
© 2024.
21 pages.
|
|
Fatema Ahmed Wali, Zahra Tammam.
© 2024.
24 pages.
|
|
Su Jiayuan, Jingru Zhang.
© 2024.
26 pages.
|
|
Pua Shiau Chen.
© 2024.
21 pages.
|
|
Minh Tung Tran, Thu Trinh Thi, Lan Duong Hoai.
© 2024.
23 pages.
|
|
|