@inproceedings{18c85d58ebe748108aaf63544189cb0f,
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 = "الإنجليزيّة",
series = "International Conference on Information and Knowledge Management, Proceedings",
pages = "3261--3264",
booktitle = "CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management",
}