Abstract
A warehouse is a data repository containing integrated information for efficient querying and analysis. Maintaining the consistency of warehouse data is challenging, especially if the data sources are autonomous and views of the data at the warehouse span multiple sources. Transactions containing multiple updates at one or more sources, e.g., batch updates, complicate the consistency problem. In this paper we identify and discuss three fundamental transaction processing scenarios for data warehousing. We define four levels of consistency for warehouse data and present a new family of algorithms, the Strobe family, that maintain consistency as the warehouse is updated, under the various warehousing scenarios. All of the algorithms are incremental and can handle a continuous and overlapping stream of updates from the sources. Our implementation shows that the algorithms are practical and realistic choices for a wide variety of update scenarios.
This work was partially supported by Rome Laboratories under Air Force Contract F30602-94-C-0237; by an equipment grant from Digital Equipment Corporation; and by the Advanced Research and Development Committee of the Community Management Staff, as a project in the Massive Digital Data Systems Program.
This paper is an extended version of a paper published in International Conference on Parallel and Distributed Information Systems, December 1996. The main additions are in Section 6.2 and Section 8. Also, we included the full proof of correctness of Strobe and C-Strobe algorithm in Appendix A. Proof of Correctness for Strobe Algorithm and B. Proof of Correctness for Complete Strobe Algorithm.
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Zhuge, Y., Garcia-Molina, H., Wiener, J.L. (1998). Consistency Algorithms for Multi-Source Warehouse View Maintenance. In: Naughton, J.F., Weikum, G. (eds) Parallel and Distributed Information Systems. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-6132-0_2
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