Conference proceeding
An analysis of additivity in OLAP systems
Proceedings of the 7th ACM international workshop on data warehousing and olap, pp 83-91
12 Nov 2004
Abstract
Accurate summary data is of paramount concern in data warehouse systems; however, there have been few attempts to completely characterize the ability to summarize measures. The sum operator is the typical aggregate operator for summarizing the large amount of data in these systems. We look to uncover and characterize potentially inaccurate summaries resulting from aggregating measures using the sum operator. We discuss the effect of classification hierarchies, and non-, semi-, and fully- additive measures on summary data, and develop a taxonomy of the additive nature of measures. Additionally, averaging and rounding rules can add complexity to seemingly simple aggregations. To deal with these problems, we describe the importance of storing metadata that can be used to restrict potentially inaccurate aggregate queries. These summary constraints could be integrated into data warehouses, just as integrity constraints and are integrated into OLTP systems. We conclude by suggesting methods for identifying and dealing with non- and semi- additive attributes.
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41 citations in Scopus
Details
- Title
- An analysis of additivity in OLAP systems
- Creators
- John HornerIl-Yeol SongPeter Chen
- Publication Details
- Proceedings of the 7th ACM international workshop on data warehousing and olap, pp 83-91
- Conference
- 7th ACM international workshop on data warehousing and olap, 7th
- Series
- DOLAP '04
- Publisher
- Association for Computing Machinery (ACM)
- Number of pages
- 1
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-19644395350
- Other Identifier
- 991014878282304721