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Article

Space efficiency in synopsis construction algorithms

Published: 30 August 2005 Publication History

Abstract

Histograms and Wavelet synopses have been found to be useful in query optimization, approximate query answering and mining. Over the last few years several good synopsis algorithms have been proposed. These have mostly focused on the running time of the synopsis constructions, optimum or approximate, vis-a-vis their quality. However the space complexity of synopsis construction algorithms has not been investigated as thoroughly. Many of the optimum synopsis construction algorithms (as well as few of the approximate ones) are expensive in space. In this paper, we propose a general technique that reduces space complexity. We show that the notion of "working space" proposed in these contexts is redundant. We believe that our algorithm also generalizes to a broader range of dynamic programs beyond synopsis construction. Our modifications can be easily adapted to existing algorithms. We demonstrate the performance benefits through experiments on real-life and synthetic data.

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  • (2022)SIEVE: A Space-Efficient Algorithm for Viterbi DecodingProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526170(1136-1145)Online publication date: 10-Jun-2022
  • (2019)Maintaining Wavelet Synopses for Sliding-Window AggregatesProceedings of the 31st International Conference on Scientific and Statistical Database Management10.1145/3335783.3335793(73-84)Online publication date: 23-Jul-2019
  • (2017)Efficient Haar+ synopsis construction for the maximum absolute error measureProceedings of the VLDB Endowment10.14778/3151113.315111711:1(40-52)Online publication date: 1-Sep-2017
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cover image DL Hosted proceedings
VLDB '05: Proceedings of the 31st international conference on Very large data bases
August 2005
1392 pages
ISBN:1595931546

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VLDB Endowment

Publication History

Published: 30 August 2005

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View all
  • (2022)SIEVE: A Space-Efficient Algorithm for Viterbi DecodingProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526170(1136-1145)Online publication date: 10-Jun-2022
  • (2019)Maintaining Wavelet Synopses for Sliding-Window AggregatesProceedings of the 31st International Conference on Scientific and Statistical Database Management10.1145/3335783.3335793(73-84)Online publication date: 23-Jul-2019
  • (2017)Efficient Haar+ synopsis construction for the maximum absolute error measureProceedings of the VLDB Endowment10.14778/3151113.315111711:1(40-52)Online publication date: 1-Sep-2017
  • (2016)Distributed Wavelet Thresholding for Maximum Error MetricsProceedings of the 2016 International Conference on Management of Data10.1145/2882903.2915230(663-677)Online publication date: 26-Jun-2016
  • (2013)Finding the minimum number of elements with sum above a thresholdInformation Sciences: an International Journal10.1016/j.ins.2013.02.035238(205-211)Online publication date: 1-Jul-2013
  • (2013)Computing Unrestricted Synopses Under Maximum Error BoundAlgorithmica10.1007/s00453-011-9571-965:1(1-42)Online publication date: 1-Jan-2013
  • (2009)Unrestricted wavelet synopses under maximum error boundProceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology10.1145/1516360.1516445(732-743)Online publication date: 24-Mar-2009
  • (2009)Tight results for clustering and summarizing data streamsProceedings of the 12th International Conference on Database Theory10.1145/1514894.1514926(268-275)Online publication date: 23-Mar-2009
  • (2009)Hierarchically compressed wavelet synopsesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-008-0096-z18:1(203-231)Online publication date: 1-Jan-2009
  • (2007)Building data synopses within a known maximum error boundProceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management10.5555/1769708.1769768(463-470)Online publication date: 16-Jun-2007
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