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Adaptive learning of aggregate analytics under dynamic workloads
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Savva, Fotis, Anagnostopoulos, Christos and Triantafillou, Peter (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 ISSN 0167-739X.
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Official URL: http://dx.doi.org/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, reciprocity-based 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 back-end. 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: | Journal Article | |||||||||
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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): | Question-answering systems, Machine learning, Decision making -- Data processing, Change-point problems | |||||||||
Journal or Publication Title: | Future Generation Computer Systems | |||||||||
Publisher: | Elsevier Science BV | |||||||||
ISSN: | 0167-739X | |||||||||
Official Date: | August 2020 | |||||||||
Dates: |
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Volume: | 109 | |||||||||
Page Range: | pp. 317-330 | |||||||||
DOI: | 10.1016/j.future.2020.03.063 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Date of first compliant deposit: | 17 June 2020 | |||||||||
Date of first compliant Open Access: | 17 June 2020 | |||||||||
RIOXX Funder/Project Grant: |
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