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Aggregate query prediction under dynamic workloads
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Savva, Fotis, Anagnostopoulos, Christos and Triantafillou, Peter (2020) Aggregate query prediction under dynamic workloads. In: 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 9-12 Dec 2019 pp. 671-676. ISBN 9781728108582. doi:10.1109/BigData47090.2019.9006267
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WRAP-aggregate-query-prediction-under-dynamic-workloads-Triantafillou-2020.pdf - Accepted Version - Requires a PDF viewer. Download (2889Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/BigData47090.2019.900626...
Abstract
Large organizations have seamlessly incorporated data-driven decision making in their operations. However, as data volumes increase, expensive big data infrastructures are called to rescue. In this setting, analytics tasks become very costly in terms of query response time, resource consumption, and money in cloud deployments, especially when base data are stored across geographically distributed data centers. Therefore, we introduce an adaptive Machine Learning mechanism which is light-weight, stored client-side, can estimate the answers of a variety of aggregate queries and can avoid the big data backend. The estimations are performed in milliseconds and are inexepensive as the mechanism learns from past analytical-query patterns. However, as analytic queries are ad-hoc and analysts' interests change over time we develop solutions that can swiftly and accurately detect such changes and adapt to new query patterns. The capabilities of our approach are demonstrated using extensive evaluation with real and synthetic datasets.
Item Type: | Conference Item (Paper) | |||||||||
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Subjects: | H Social Sciences > HD Industries. Land use. Labor Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
Library of Congress Subject Headings (LCSH): | Decision making -- Data processing, Data mining, Machine learning , Artificial intelligence -- Computer programs, Change-point problems | |||||||||
Publisher: | IEEE | |||||||||
ISBN: | 9781728108582 | |||||||||
Book Title: | 2019 IEEE International Conference on Big Data (Big Data) | |||||||||
Official Date: | 24 February 2020 | |||||||||
Dates: |
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Page Range: | pp. 671-676 | |||||||||
DOI: | 10.1109/BigData47090.2019.9006267 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Reuse Statement (publisher, data, author rights): | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 22 June 2020 | |||||||||
Date of first compliant Open Access: | 23 June 2020 | |||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | |||||||||
Title of Event: | 2019 IEEE International Conference on Big Data (Big Data) | |||||||||
Type of Event: | Conference | |||||||||
Location of Event: | Los Angeles, CA, USA | |||||||||
Date(s) of Event: | 9-12 Dec 2019 |
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