A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
AI Meets AI
2019
Proceedings of the 2019 International Conference on Management of Data - SIGMOD '19
State-of-the-art index tuners rely on query optimizer's cost estimates to search for the index configuration with the largest estimated execution cost improvement. Due to wellknown limitations in optimizer's estimates, in a significant fraction of cases, an index estimated to improve a query's execution cost, e.g., CPU time, makes that worse when implemented. Such errors are a major impediment for automated indexing in production systems. We observe that comparing the execution cost of two
doi:10.1145/3299869.3324957
dblp:conf/sigmod/DingDM0CN19
fatcat:kaho47sbtbc6tc57c6j3edwwzi