Optimal End-Biased Histograms for Hierarchical Data

Rachel Behar, Sara Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We focus on summarizing hierarchical data by adapting the well-known notion of end biased-histograms to trees. Over relational data, such histograms have been well-studied, as they have a good balance between accuracy and space requirements. Extending histograms to tree data is a non-trivial problem, due to the need to preserve and leverage structure in the output. We develop a fast greedy algorithm, and a polynomial algorithm that finds provably optimal hierarchical end-biased histograms. Preliminary experimentation demonstrates that our histograms work well in practice.

Original languageAmerican English
Title of host publicationCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
Pages3261-3264
Number of pages4
ISBN (Electronic)9781450368599
DOIs
StatePublished - 19 Oct 2020
Event29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
Duration: 19 Oct 202023 Oct 2020

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
Country/TerritoryIreland
CityVirtual, Online
Period19/10/2023/10/20

Keywords

  • end-biased
  • hierarchical data
  • histograms

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • General Business,Management and Accounting

Fingerprint

Dive into the research topics of 'Optimal End-Biased Histograms for Hierarchical Data'. Together they form a unique fingerprint.

Cite this