Heuristic and Cost-based Optimization for Diverse Provenance Tasks
Abstract
A well-established technique for capturing database provenance as annotations on data is to instrument queries to propagate such annotations. However, even sophisticated query optimizers often fail to produce efficient execution plans for instrumented queries. We develop provenance-aware optimization techniques to address this problem. Specifically, we study algebraic equivalences targeted at instrumented queries and alternative ways of instrumenting queries for provenance capture. Furthermore, we present an extensible heuristic and cost-based optimization framework utilizing these optimizations. Our experiments confirm that these optimizations are highly effective, improving performance by several orders of magnitude for diverse provenance tasks.
Cite
@article{arxiv.1804.07156,
title = {Heuristic and Cost-based Optimization for Diverse Provenance Tasks},
author = {Xing Niu and Raghav Kapoor and Boris Glavic and Dieter Gawlick and Zhen Hua Liu and Vasudha Krishnaswamy and Venkatesh Radhakrishnan},
journal= {arXiv preprint arXiv:1804.07156},
year = {2018}
}
Comments
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018, long version, 31 pages. arXiv admin note: substantial text overlap with arXiv:1701.05513