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SuRF : identification of interesting data regions with surrogate models
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Savva, Fotis, Anagnostopoulos, Christos and Triantafillou, Peter (2020) SuRF : identification of interesting data regions with surrogate models. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, USA, 20-24 Apr 2020. Published in: 2020 IEEE 36th International Conference on Data Engineering (ICDE) pp. 1321-1332. ISBN 9781728129044. doi:10.1109/ICDE48307.2020.00118 ISSN 1063-6382.
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Official URL: http://dx.doi.org/10.1109/ICDE48307.2020.00118
Abstract
Several data mining tasks focus on repeatedly inspecting multidimensional data regions summarized by a statistic. The value of this statistic (e.g., region-population sizes, order moments) is used to classify the region’s interesting-ness. These regions can be naively extracted from the entire dataspace – however, this is extremely time-consuming and compute-resource demanding. This paper studies the reverse problem: analysts provide a cut-off value for a statistic of interest and in turn our proposed framework efficiently identifies multidimensional regions whose statistic exceeds (or is below) the given cut-off value (according to user’s needs). However, as data dimensions and size increase, such task inevitably becomes laborious and costly. To alleviate this cost, our solution, coined SuRF (SUrrogate Region Finder), leverages historical region evaluations to train surrogate models that learn to approximate the distribution of the statistic of interest. It then makes use of evolutionary multi-modal optimization to effectively and efficiently identify regions of interest regardless of data size and dimensionality. The accuracy, efficiency, and scalability of our approach are demonstrated with experiments using synthetic and real-world datasets and compared with other methods.
Item Type: | Conference Item (Paper) | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Journal or Publication Title: | 2020 IEEE 36th International Conference on Data Engineering (ICDE) | ||||||
Publisher: | IEEE | ||||||
ISBN: | 9781728129044 | ||||||
ISSN: | 1063-6382 | ||||||
Book Title: | 2020 IEEE 36th International Conference on Data Engineering (ICDE) | ||||||
Official Date: | 27 May 2020 | ||||||
Dates: |
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Page Range: | pp. 1321-1332 | ||||||
DOI: | 10.1109/ICDE48307.2020.00118 | ||||||
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 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | 2020 IEEE 36th International Conference on Data Engineering (ICDE) | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Dallas, TX, USA | ||||||
Date(s) of Event: | 20-24 Apr 2020 |
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