Optimal End-Biased Histograms for Hierarchical Data

Rachel Behar; Sara Cohen

doi:10.1145/3340531.3417449

Optimal End-Biased Histograms for Hierarchical Data

Rachel Behar, Sara Cohen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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 language	English
Title of host publication	CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
Publisher	Association for Computing Machinery
Pages	3261-3264
Number of pages	4
ISBN (Electronic)	9781450368599
DOIs	https://doi.org/10.1145/3340531.3417449
State	Published - 19 Oct 2020
Event	29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland Duration: 19 Oct 2020 → 23 Oct 2020

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings

Conference

Conference	29th ACM International Conference on Information and Knowledge Management, CIKM 2020
Country/Territory	Ireland
City	Virtual, Online
Period	19/10/20 → 23/10/20

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Keywords

end-biased
hierarchical data
histograms

Access to Document

10.1145/3340531.3417449

Cite this

@inproceedings{4515ff1c1d874172b808caa62dd94134,

title = "Optimal End-Biased Histograms for Hierarchical Data",

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.",

keywords = "end-biased, hierarchical data, histograms",

author = "Rachel Behar and Sara Cohen",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 ; Conference date: 19-10-2020 Through 23-10-2020",

year = "2020",

month = oct,

day = "19",

doi = "10.1145/3340531.3417449",

language = "אנגלית",

series = "International Conference on Information and Knowledge Management, Proceedings",

publisher = "Association for Computing Machinery",

pages = "3261--3264",

booktitle = "CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management",

}

Behar, R & Cohen, S 2020, Optimal End-Biased Histograms for Hierarchical Data. in CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, Association for Computing Machinery, pp. 3261-3264, 29th ACM International Conference on Information and Knowledge Management, CIKM 2020, Virtual, Online, Ireland, 19/10/20. https://doi.org/10.1145/3340531.3417449

Optimal End-Biased Histograms for Hierarchical Data. / Behar, Rachel ; Cohen, Sara.
CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, 2020. p. 3261-3264 (International Conference on Information and Knowledge Management, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Optimal End-Biased Histograms for Hierarchical Data

AU - Behar, Rachel

AU - Cohen, Sara

PY - 2020/10/19

Y1 - 2020/10/19

N2 - 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.

AB - 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.

KW - end-biased

KW - hierarchical data

KW - histograms

UR - https://www.scopus.com/pages/publications/85095865145

U2 - 10.1145/3340531.3417449

DO - 10.1145/3340531.3417449

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85095865145

T3 - International Conference on Information and Knowledge Management, Proceedings

SP - 3261

EP - 3264

BT - CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management

PB - Association for Computing Machinery

T2 - 29th ACM International Conference on Information and Knowledge Management, CIKM 2020

Y2 - 19 October 2020 through 23 October 2020

ER -

Optimal End-Biased Histograms for Hierarchical Data

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this