Department of Computer Science
Side-effect estimation: A filtering approach to the view update problem
© 1989-2012 IEEE.Views and their updates have long been a fundamental technology required in a wide range of applications. However, it has been known that updates through views is a classical intractable problem. In this paper, we propose a novel, data-oriented approach to this problem that provides a practical support for view updates. In particular, we propose a summarization of the source database of views, which serves as an update filter. The update filter aims to efficiently reject untranslatable view updates by estimating the side effects of the updates, thereby avoiding costly translation analysis. For applications where estimation errors are not preferred, our update filter can be tuned to be exact. In this paper, we present our approach with SPJ views, an important class of view definitions. We first revise the notion of estimation errors to quantify the filter's qualities. We then propose a novel join cardinality summary (JCard) derived from cardinality equivalence. An estimation algorithm is proposed. Finally, we present optimizations enabling the construction of an accurate JCard through heuristics and sampling. Our extensive experiments show that update filters are efficient and can be easily tuned to produce accurate estimations on TPC-H and DBLP.
Database Management, Relational databases
Source Publication Title
IEEE Transactions on Knowledge and Data Engineering
Institute of Electrical and Electronics Engineers
Peng, Yun, Byron Choi, Jianliang Xu, Haibo Hu, and Sourav S. Bhowmick. "Side-effect estimation: A filtering approach to the view update problem." IEEE Transactions on Knowledge and Data Engineering 26.9 (2014): 2307-2322.