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An active warehouse is refreshed on-line and thus achieves a higher consistency between the stored information and the latest data updates. The need for on-line warehouse refreshment introduces several challenges in the implementation of data warehouse transformations. In this article, we focus on a frequently encountered operation in this context, namely, the join of a fast stream $S$ of source updates with a disk-based relation $R$, under the constraint of limited memory. This operation lies at the core of several common transformations, such as, surrogate key assignment, duplicate detection or identification of newly inserted tuples. We propose a specialized join algorithm, termed MeshJoin , that compensates for the difference in the access cost of the two join inputs by (a) relying entirely on fast sequential scans of $R$, and (b) sharing the I/O cost of accessing $R$ across multiple tuples of $S$. We detail the MeshJoin algorithm and develop a systematic cost model that enables tuning MeshJoin based on the available memory and the desired throughput. We present an experimental study that validates the performance of MeshJoin on synthetic and real-life data. Our results verify the effectiveness of MeshJoin and demonstrate its advantages over existing join algorithms.
Query processing, Data warehouse and repository

A. Simitsis, P. Vassiliadis, N. Polyzotis, N. Frantzell and S. Skiadopoulos, "Meshing Streaming Updates with Persistent Data in an Active Data Warehouse," in IEEE Transactions on Knowledge & Data Engineering, vol. 20, no. , pp. 976-991, 2008.
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