Aggregate Query Prediction under Dynamic Workloads

Savva, F. , Anagnostopoulos, C. and Triantafillou, P. (2020) Aggregate Query Prediction under Dynamic Workloads. In: 2019 IEEE International Conference on Big Data (IEEE BigData 2019), Los Angeles, CA, USA, 09-12 Dec 2019, pp. 671-676. ISBN 9781728108582 (doi:10.1109/BigData47090.2019.9006267)

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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 Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Anagnostopoulos, Dr Christos and Triantafillou, Professor Peter and Savva, Mr Fotis
Authors: Savva, F., Anagnostopoulos, C., and Triantafillou, P.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781728108582
Copyright Holders:Copyright © 2019 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
301654Intelligent Applications over Large Scale Data StreamsChristos AnagnostopoulosEuropean Commission (EC)745829Computing Science
300982Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science