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Explaining aggregates for exploratory analytics
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Savva, Fotis, Anagnostopoulos, Christos and Triantafillou, Peter (2018) Explaining aggregates for exploratory analytics. In: 2018 IEEE International Conference on Big Data (Big Data), Seattle, USA, 10-14 Dec 2018. Published in: 2018 IEEE International Conference on Big Data (Big Data)
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WRAP-explaining-aggregates-exploratory-analytics-Triantafillou-2018.pdf - Accepted Version - Requires a PDF viewer. Download (1026Kb) | Preview |
Official URL: http://cci.drexel.edu/bigdata/bigdata2018/Accepted...
Abstract
Analysts wishing to explore multivariate data spaces, typically pose queries involving selection operators, i.e., range or radius queries, which define data subspaces of possible interest and then use aggregation functions, the results of which determine their exploratory analytics interests. However, such aggregate query (AQ) results are simple scalars and as such, convey limited information about the queried subspaces for exploratory analysis. We address this shortcoming aiding analysts to explore and understand data subspaces by contributing a novel explanation mechanism coined XAXA: eXplaining Aggregates for eXploratory Analytics. XAXA’s novel AQ explanations are repre- sented using functions obtained by a three-fold joint optimization problem. Explanations assume the form of a set of parametric piecewise-linear functions acquired through a statistical learning model. A key feature of the proposed solution is that model training is performed by only monitoring AQs and their answers on-line. In XAXA, explanations for future AQs can be computed without any database (DB) access and can be used to further explore the queried data subspaces, without issuing any more queries to the DB. We evaluate the explanation accuracy and efficiency of XAXA through theoretically grounded metrics over real-world and synthetic datasets and query workloads.
Item Type: | Conference Item (Paper) | ||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||
Library of Congress Subject Headings (LCSH): | Algorithms, Machine learning | ||||
Journal or Publication Title: | 2018 IEEE International Conference on Big Data (Big Data) | ||||
Publisher: | IEEE | ||||
Official Date: | 25 October 2018 | ||||
Dates: |
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Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Reuse Statement (publisher, data, author rights): | © 2018 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: | 3 January 2019 | ||||
Date of first compliant Open Access: | 8 January 2019 | ||||
Conference Paper Type: | Paper | ||||
Title of Event: | 2018 IEEE International Conference on Big Data (Big Data) | ||||
Type of Event: | Conference | ||||
Location of Event: | Seattle, USA | ||||
Date(s) of Event: | 10-14 Dec 2018 | ||||
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