Aqua: A Fast Decision Support Systems Using Approximate Query Answers.

Swarup Acharya, Phillip B. Gibbons, Viswanath Poosala: Aqua: A Fast Decision Support Systems Using Approximate Query Answers. VLDB 1999: 754-757

@inproceedings{DBLP:conf/vldb/AcharyaGP99,
  author    = {Swarup Acharya and
               Phillip B. Gibbons and
               Viswanath Poosala},
  editor    = {Malcolm P. Atkinson and
               Maria E. Orlowska and
               Patrick Valduriez and
               Stanley B. Zdonik and
               Michael L. Brodie},
  title     = {Aqua: A Fast Decision Support Systems Using Approximate Query
               Answers},
  booktitle = {VLDB'99, Proceedings of 25th International Conference on Very
               Large Data Bases, September 7-10, 1999, Edinburgh, Scotland,
               UK},
  publisher = {Morgan Kaufmann},
  year      = {1999},
  isbn      = {1-55860-615-7},
  pages     = {754-757},
  ee        = {db/conf/vldb/AcharyaGP99.html},
  crossref  = {DBLP:conf/vldb/99},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Aqua is a system for providing fast, approximate answers to aggregate queries, which are very common in OLAP applications. It has been designed to run on top of any commercial relational DBMS. Aqua precomputes synopses (special statistical summaries) of the original data and stores them in the DBMS. It provides approximate answers (with quality guarantees) by rewriting the queries to run on these synopses. Finally, Aqua also incrementally keeps the synopses up-to-date as the database changes.

Copyright © 1999 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

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Printed Edition

Malcolm P. Atkinson, Maria E. Orlowska, Patrick Valduriez, Stanley B. Zdonik, Michael L. Brodie (Eds.): VLDB'99, Proceedings of 25th International Conference on Very Large Data Bases, September 7-10, 1999, Edinburgh, Scotland, UK. Morgan Kaufmann 1999, ISBN 1-55860-615-7
Contents

References

[AGP99]: ...
[AGPR99]: Swarup Acharya, Phillip B. Gibbons, Viswanath Poosala, Sridhar Ramaswamy: Join Synopses for Approximate Query Answering. SIGMOD Conference 1999: 275-286
[GM98]: Phillip B. Gibbons, Yossi Matias: New Sampling-Based Summary Statistics for Improving Approximate Query Answers. SIGMOD Conference 1998: 331-342
[GMP97]: Phillip B. Gibbons, Yossi Matias, Viswanath Poosala: Fast Incremental Maintenance of Approximate Histograms. VLDB 1997: 466-475
[HHW97]: Joseph M. Hellerstein, Peter J. Haas, Helen J. Wang: Online Aggregation. SIGMOD Conference 1997: 171-182
[IP99]: Yannis E. Ioannidis, Viswanath Poosala: Histogram-Based Approximation of Set-Valued Query-Answers. VLDB 1999: 174-185
[PG99]: Viswanath Poosala, Venkatesh Ganti: Fast Approximate Answers to Aggregate Queries on a Data Cube. SSDBM 1999: 24-33
[VL93]: Susan V. Vrbsky, Jane W.-S. Liu: APPROXIMATE - A Query Processor that Produces Monotonically Improving Approximate Answers. IEEE Trans. Knowl. Data Eng. 5(6): 1056-1068(1993)
[VWI98]: Jeffrey Scott Vitter, Min Wang, Balakrishna R. Iyer: Data Cube Approximation and Histograms via Wavelets. CIKM 1998: 96-104