Adaptive learning of aggregate analytics under dynamic workloads

Savva, F. , Anagnostopoulos, C. and Triantafillou, P. (2020) Adaptive learning of aggregate analytics under dynamic workloads. Future Generation Computer Systems, 109, pp. 317-330. (doi: 10.1016/j.future.2020.03.063)

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 are inexpensive and accurate 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: Articles
• 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
• Journal Name: Future Generation Computer Systems
• Publisher: Elsevier
• ISSN: 0167-739X
• ISSN (Online): 1872-7115
• Published Online: 07 April 2020
• Copyright Holders: Copyright © 2020 The Authors
• First Published: First published in Future Generation Computer Systems 109:317-330
• Publisher Policy: Reproduced under a Creative Commons license

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Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
301654		Intelligent Applications over Large Scale Data Streams	Christos Anagnostopoulos	European Commission (EC)	745829	Computing Science
300982		Exploiting Closed-Loop Aspects in Computationally and Data Intensive Analytics	Roderick Murray-Smith	Engineering and Physical Sciences Research Council (EPSRC)	EP/R018634/1	Computing Science