Approximate Queries over Concurrent Updates

Congying Wang, Nithin Sastry Tellapuri, Sphoorthi Keshannagari, Dylan Zinsley, Zhuoyue Zhao, Dong Xie

Research output: Contribution to journalConference articlepeer-review

Abstract

Approximate Query Processing (AQP) systems produce estimation of query answers using small random samples. It is attractive for the users who are willing to trade accuracy for low query latency. On the other hand, real-world data are often subject to concurrent updates. If the user wants to perform real-time approximate data analysis, the AQP system must support concurrent updates and sampling. Towards that, we recently developed a new concurrent index, AB-tree, to support efficient sampling under updates. In this work, we will demonstrate the feasibility of supporting realtime approximate data analysis in online transaction settings using index-assisted sampling.

Original languageEnglish (US)
Pages (from-to)3986-3989
Number of pages4
JournalProceedings of the VLDB Endowment
Volume16
Issue number12
DOIs
StatePublished - 2023
Event49th International Conference on Very Large Data Bases, VLDB 2023 - Vancouver, Canada
Duration: Aug 28 2023Sep 1 2023

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • General Computer Science

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