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A Conceptual Model and Algebra for On-Line Analytical Processing in Decision Support Databases

Author

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  • Helen Thomas

    (DuPree College of Management, Georgia Institute of Technology, Atlanta, Georgia 30318-0520)

  • Anindya Datta

    (DuPree College of Management, Georgia Institute of Technology, Atlanta, Georgia 30318-0520)

Abstract

Data warehousing and On-Line Analytical Processing (OLAP) are two of the most significant new technologies in the business data processing arena. A data warehouse, or decision support database, can be defined as a “very large” repository of historical data pertaining to an organization. OLAP refers to the technique of performing complex analysis over the information stored in a data warehouse. The complexity of queries required to support OLAP applications makes it difficult to implement using standard relational database technology. Moreover, currently there is no standard conceptual model for OLAP. There clearly is a need for such a model and an algebra as evidenced by the numerous SQL extensions offered by many vendors of OLAP products. In this paper we address this issue by proposing a model of a data cube and an algebra to support OLAP operations on this cube. The model we present is simple and intuitive, and the algebra provides a means to concisely express complex OLAP queries.

Suggested Citation

  • Helen Thomas & Anindya Datta, 2001. "A Conceptual Model and Algebra for On-Line Analytical Processing in Decision Support Databases," Information Systems Research, INFORMS, vol. 12(1), pages 83-102, March.
    • Handle: RePEc:inm:orisre:v:12:y:2001:i:1:p:83-102
    DOI: 10.1287/isre.12.1.83.9715
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    Cited by:

    1. Olãh Judit & Erdei Edina & Popp Jozsef, 2017. "Applying Big Data Algorithms For Sales Data Stored In Sap Hana," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 453-461, July.
    2. Huang, Chun-Che & (Bill) Tseng, Tzu-Liang & Li, Ming-Zhong & Gung, Roger R., 2006. "Models of multi-dimensional analysis for qualitative data and its application," European Journal of Operational Research, Elsevier, vol. 174(2), pages 983-1008, October.
    3. Gediminas Adomavicius & Alexander Tuzhilin & Rong Zheng, 2011. "REQUEST: A Query Language for Customizing Recommendations," Information Systems Research, INFORMS, vol. 22(1), pages 99-117, March.

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