The Comparison of Anchor and Star Schema from a Query Performance Perspective

Authors: Radek Němec

Abstract:

Today's business environment requires that companies have access to highly relevant information in a matter of seconds. Modern Business Intelligence tools rely on data structured mostly in traditional dimensional database schemas, typically represented by star schemas. Dimensional modeling is already recognized as a leading industry standard in the field of data warehousing although several drawbacks and pitfalls were reported. This paper focuses on the analysis of another data warehouse modeling technique - the anchor modeling, and its characteristics in context with the standardized dimensional modeling technique from a query performance perspective. The results of the analysis show information about performance of queries executed on database schemas structured according to principles of each database modeling technique.

Keywords: Data warehousing, anchor modeling, star schema, anchor schema, query performance.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1331351

References:

[1] S. Ambler, Agile Database Techniques: Effective Strategies for the Agile Software Developer,New Jersey: Wiley, 2003.
[2] O. Regardt, L. Rönnb├ñck, M. Bergholtz, P. Johannesson, P. Wohed, "Anchor Modeling: An Agile Modeling Technique Using the Sixth Normal Form for Structurally and Temporally Evolving Data", 2009, ER 2009
[Lecture Notes in Computer Science, vol. 5829,no.1, pp. 234-250].
[3] H. J. Watson, T. Ariyachandra. Data Warehouse Architectures: Factors in the Selection Decision and the Success of the Architectures, Technical Report, Terry College of Business, University of Georgia, Athens, GA, July 2005
[4] C. J. Date, The Relational Database Dictionary: A Comprehensive Glossary of Relational Terms and Concepts, with Illustrative Examples,2006, O'Reilly Series Pocket References. O'Reilly Media, Inc.
[5] R. Rob, C. Coronel, K. Crockett, Database Systems: Design, Implementation & Management, 2008, London: Cengage Learning EMEA.
[6] A. Askarunisa, P. Prameela, N. Ramraj, "DBGEN- Database (Test) GENerator - An Automated Framework for Database Application Testing". 2009, International Journal of Database Theory and Application, vol. 2, no. 3, pp. 27-54.
[7] G. Di Vitantonio, J. Legh-Smith, W. Millar, M. Wilkinson, "Meeting business objectives through adaptive information and communications technology", 2006, BT Technology Journal, vol. 24, no. 4, pp. 113-120.