Histogram domain ordering for path selectivity estimation

N. Yakovets, Li Wang, G.H.L. Fletcher, Craig Taverner, Alexandra Poulovassilis

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Abstract

We aim to improve the accuracy of path selectivity estimation in graph databases by intelligently ordering the domain of a histogram used for estimation. This problem has not, to our knowledge, received adequate attention in the research community. We present a novel framework for the systematic study of path ordering strategies in histogram construction and use. In this framework, we introduce new ordering strategies which we experimentally demonstrate lead to significant improvement of the accuracy of path selectivity estimation over current strategies. These positive results highlight the fundamental role that domain ordering plays in the design of effective histograms for efficient and scalable graph query processing.

Original languageEnglish
Title of host publicationAdvances in Database Technology - EDBT 2018
Subtitle of host publication21st International Conference on Extending Database Technology, Proceedings
EditorsMichael Bohlen, Reinhard Pichler, Norman May, Erhard Rahm, Shan-Hung Wu, Katja Hose
PublisherOpenProceedings.org
Pages493-496
Number of pages4
ISBN (Electronic)978-3-89318-078-3
DOIs
Publication statusPublished - 2018
EventEDBT/ICDT 2018 Joint Conference 21st International Conference on Extending Database Technology - Vienna, Austria
Duration: 26 Mar 201829 Mar 2018

Conference

ConferenceEDBT/ICDT 2018 Joint Conference 21st International Conference on Extending Database Technology
CountryAustria
CityVienna
Period26/03/1829/03/18

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    Yakovets, N., Wang, L., Fletcher, G. H. L., Taverner, C., & Poulovassilis, A. (2018). Histogram domain ordering for path selectivity estimation. In M. Bohlen, R. Pichler, N. May, E. Rahm, S-H. Wu, & K. Hose (Eds.), Advances in Database Technology - EDBT 2018: 21st International Conference on Extending Database Technology, Proceedings (pp. 493-496). OpenProceedings.org. https://doi.org/10.5441/002/edbt.2018.55