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Maintaining Dimension's History in Data Warehouses Effectively

Maintaining Dimension's History in Data Warehouses Effectively

Canan Eren Atay, Georgia Garani
Copyright: © 2019 |Volume: 15 |Issue: 3 |Pages: 17
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781522564201|DOI: 10.4018/IJDWM.2019070103
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MLA

Atay, Canan Eren, and Georgia Garani. "Maintaining Dimension's History in Data Warehouses Effectively." IJDWM vol.15, no.3 2019: pp.46-62. http://doi.org/10.4018/IJDWM.2019070103

APA

Atay, C. E. & Garani, G. (2019). Maintaining Dimension's History in Data Warehouses Effectively. International Journal of Data Warehousing and Mining (IJDWM), 15(3), 46-62. http://doi.org/10.4018/IJDWM.2019070103

Chicago

Atay, Canan Eren, and Georgia Garani. "Maintaining Dimension's History in Data Warehouses Effectively," International Journal of Data Warehousing and Mining (IJDWM) 15, no.3: 46-62. http://doi.org/10.4018/IJDWM.2019070103

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Abstract

A data warehouse is considered a key aspect of success for any decision support system. Research on temporal databases have produced important results in this field, and data warehouses, which store historical data, can clearly benefit from such studies. A slowly changing dimension is a dimension in which any of its attributes in a data warehouse can change infrequently over time. Although different solutions have been proposed, each has its own particular disadvantages. The authors propose the Object-Relational Temporal Data Warehouse (O-RTDW) model for the slowly changing dimensions in this research work. Using this approach, it is possible to keep track of the whole history of an object in a data warehouse efficiently. The proposed model has been implemented on a real data set and tested successfully. Several limitations implied in other solutions, such as redundancy, surrogate keys, incomplete historical data, and creation of additional tables are not present in our solution.

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