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DBEst : revisiting approximate query processing engines with machine learning models
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Ma, Qingzhi and Triantafillou, Peter (2019) DBEst : revisiting approximate query processing engines with machine learning models. In: 2019 ACM International Conference on the Management of Data, SIGMOD19, Amsterdam, Netherlands, 30 Jun - 5 Jul 2019. Published in: SIGMOD '19 Proceedings of the 2019 International Conference on Management of Data 1553-1570 . ISBN 9781450356435. doi:10.1145/3299869.3324958
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Official URL: https://doi.org/10.1145/3299869.3324958
Abstract
In the era of big data, computing exact answers to analytical queries becomes prohibitively expensive. This greatly increases the value of approaches that can compute efficiently approximate, but highly-accurate, answers to analytical queries. Alas, the state of the art still suffers from many shortcomings: Errors are still high, unless large memory investments are made. Many important analytics tasks are not supported. Query response times are too long and thus approaches rely on parallel execution of queries atop large big data analytics clusters, in-situ or in the cloud, whose acquisition/use costs dearly. Hence, the following questions are crucial: Can we develop AQP engines that reduce response times by orders of magnitude, ensure high accuracy, and support most aggregate functions? With smaller memory footprints and small overheads to build the state upon which they are based? With this paper, we show that the answers to all questions above can be positive. The paper presents DBEst, a system based on Machine Learning models (regression models and probability density estimators). It will discuss its limitations, promises, and how it can complement existing systems. It will substantiate its advantages using queries and data from the TPC-DS benchmark and real-life datasets, compared against state of the art AQP engines.
Item Type: | Conference Item (Paper) | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||
Journal or Publication Title: | SIGMOD '19 Proceedings of the 2019 International Conference on Management of Data | ||||
Publisher: | ACM | ||||
ISBN: | 9781450356435 | ||||
Official Date: | 1 July 2019 | ||||
Dates: |
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Page Range: | 1553-1570 | ||||
DOI: | 10.1145/3299869.3324958 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Reuse Statement (publisher, data, author rights): | © ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in..., http://dx.doi.org/10.1145/{number}." | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Date of first compliant deposit: | 10 April 2019 | ||||
Date of first compliant Open Access: | 15 August 2019 | ||||
Conference Paper Type: | Paper | ||||
Title of Event: | 2019 ACM International Conference on the Management of Data, SIGMOD19 | ||||
Type of Event: | Conference | ||||
Location of Event: | Amsterdam, Netherlands | ||||
Date(s) of Event: | 30 Jun - 5 Jul 2019 | ||||
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