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At-the-time and Back-in-time Persistent Sketches

  • Benwei Shi
  • , Zhuoyue Zhao
  • , Yanqing Peng
  • , Feifei Li
  • , Jeff M. Phillips

Research output: Contribution to journalConference articlepeer-review

18 Scopus citations

Abstract

In the era of big data, more and more applications require the information of historical data to support rich analytics, learning, and mining operations. In these cases, it is highly desirable to retrieve information of previous versions of data. Traditionally, multi-version databases can be used to store all historical values of the data in order to support historical queries. However, storing all the historical data can be impractical due to its large space consumption. In this paper, we propose the concept of at-the-time persistent (ATTP) and back-in-time persistent (BITP) sketches, which are sketches that approximately answer queries on previous versions of data with small space. We then provide several implementations of ATTP/BITP sketches which are shown to be more efficient compared to existing state-of-the-art solutions in our empirical studies.

Original languageEnglish
Pages (from-to)1623-1636
Number of pages14
JournalProceedings of the ACM SIGMOD International Conference on Management of Data
DOIs
StatePublished - 2021
Event2021 International Conference on Management of Data, SIGMOD 2021 - Virtual, Online, China
Duration: Jun 20 2021Jun 25 2021

Keywords

  • data sketching
  • persistent data structure
  • random sampling
  • streaming algorithms

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