Definition
A data warehouse (DW) collects large amounts of data from various data sources and transforms them to a form that can be used to analyze the behavior of an organization. DWs are based on the multidimensional model, which represents data as facts that can be analyzed along a collection of dimensions, composed of levels conforming aggregation hierarchies. The basic multidimensional model assumes that only facts evolve in time and this is materialized by the link(s) of the facts with the time dimension. However, dimension data may also vary across time, for instance, a product may change its price or its category. Furthermore, a measure itself can change its value, for instance, a client may request a change in the quantity of products of an order she previously placed. Temporal databases provide mechanisms for managing information that varies over time. The use of these mechanisms to provide built-in temporal semantics to DWs leads to the concept of temporal data warehouses.
Hi...
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmed W, Zimányi E, Wrembel R. Temporal data warehouses: logical models and querying. In: Proceedings of the Journées francophones sur les Entrepôts de Données et l’Analyse en ligne, EDA. Editions Hermann; 2015. p. 33–48.
Ahmed W, Zimányi E, Wrembel R. A logical model for multiversion data warehouses. In: Proceedings of the 16th International Conference on Data Warehousing and Knowledge Discovery; 2014. p. 23–34.
Böhlen M, Gamper J, Jensen C. Towards general temporal aggregation. In: Proceedings of the 25th British National Conference on Databases; 2008. p. 257–69.
Bruckner R, Min Tjoa A. Capturing delays and valid times in data warehouses: towards timely consistent analyses. J Intell Inf Syst. 2002;19(2):169–90.
Golfarelli M, Lechtenbörger J, Rizzi S, Vossen G. Schema versioning in data warehouses: enabling cross-version querying via schema augmentation. Data Knowl Eng. 2006;59(2):435–59.
Golfarelli M, Rizzi S. Managing late measurements in data warehouses. Int J Data Wareh Min. 2007;3(4):51–67.
Golfarelli M, Rizzi S. A survey on temporal data warehousing. Int J Data Wareh Min. 2009;5(1):1–17.
Johnston T, Weis R. Managing time in relational databases: how to design, update and query temporal data. Burlington: Morgan Kaufmann; 2010.
Kimball R, Ross M. The data warehouse toolkit: the complete guide to dimensional modeling. 3rd ed. Hoboken: Wiley; 2013.
Kulkarni K, Michels J-E. Temporal features in SQL:2011. SIGMOD Rec. 2012;41(3):34–43.
Malinowski E, Zimányi E. Advanced data warehouse design: from conventional to spatial and temporal applications. Berlin/Heidelberg: Springer; 2008.
Mendelzon A, Vaisman A. Time in multidimensional databases. In: Rafanelli M, editor. Multidimensional databases: problems and solutions. Hershey: Idea Group; 2003. p. 166–99.
Snodgrass R, editor. The TSQL2 temporal query language. Boston/London: Kluwer Academic; 1995.
Snodgrass R. Developing time-oriented database applications in SQL. San Francisco: Morgan Kaufmann; 2000.
Tansel AU, Clifford J, Gadia S, Jajodia S, Segev A, Snodgrass RT, editors. Temporal databases: theory, design, and implementation. Redwood City: Benjamin-Cummings; 1993.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media, LLC, part of Springer Nature
About this entry
Cite this entry
Vaisman, A.A., Zimányi, E. (2018). Temporal Datawarehousing. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_80809
Download citation
DOI: https://doi.org/10.1007/978-1-4614-8265-9_80809
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-8266-6
Online ISBN: 978-1-4614-8265-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering