Efficient Scalable Accurate Regression Queries in In-DBMS Analytics

Anagnostopoulos, C. and Triantafillou, P. (2017) Efficient Scalable Accurate Regression Queries in In-DBMS Analytics. In: IEEE International Conference on Data Engineering (ICDE), San Diego, CA, USA, 19-22 Apr 2017, pp. 559-570. ISBN 9781509065431 (doi: 10.1109/ICDE.2017.111)

136690.pdf - Accepted Version



Recent trends aim to incorporate advanced data analytics capabilities within DBMSs. Linear regression queries are fundamental to exploratory analytics and predictive modeling. However, computing their exact answers leaves a lot to be desired in terms of efficiency and scalability. We contribute a novel predictive analytics model and associated regression query processing algorithms, which are efficient, scalable and accurate. We focus on predicting the answers to two key query types that reveal dependencies between the values of different attributes: (i) mean-value queries and (ii) multivariate linear regression queries, both within specific data subspaces defined based on the values of other attributes. Our algorithms achieve many orders of magnitude improvement in query processing efficiency and nearperfect approximations of the underlying relationships among data attributes.

Item Type:Conference Proceedings
Additional Information:Gas Sensor Array Drift Dataset at Different Concentrations Data Set [Dataset Link] https://archive.ics.uci.edu/ml/datasets/Gas+Sensor+Array+Drift+Dataset+at+Different+Concentrations
Glasgow Author(s) Enlighten ID:Anagnostopoulos, Dr Christos and Triantafillou, Professor Peter
Authors: Anagnostopoulos, C., and Triantafillou, P.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2017 IEEE
First Published:First published in 2017 IEEE 33rd International Conference on Data Engineering (ICDE): 559-570
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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