Query-driven learning for predictive analytics of data subspace cardinality

Anagnostopoulos, C. and Triantafillou, P. (2017) Query-driven learning for predictive analytics of data subspace cardinality. ACM Transactions on Knowledge Discovery from Data, 11(4), 47. (doi: 10.1145/3059177)

Abstract

Fundamental to many predictive analytics tasks is the ability to estimate the cardinality (number of data items) of multi-dimensional data subspaces, defined by query selections over datasets. This is crucial for data analysts dealing with, e.g., interactive data subspace explorations, data subspace visualizations, and in query processing optimization. However, in many modern data systems, predictive analytics may be (i) too costly money-wise, e.g., in clouds, (ii) unreliable, e.g., in modern Big Data query engines, where accurate statistics are difficult to obtain/maintain, or (iii) infeasible, e.g., for privacy issues. We contribute a novel, query-driven, function estimation model of analyst-defined data subspace cardinality. The proposed estimation model is highly accurate in terms of prediction and accommodating the well-known selection queries: multi-dimensional range and distance-nearest neighbors (radius) queries. Our function estimation model: (i) quantizes the vectorial query space, by learning the analysts’ access patterns over a data space, (ii) associates query vectors with their corresponding cardinalities of the analyst-defined data subspaces, (iii) abstracts and employs query vectorial similarity to predict the cardinality of an unseen/unexplored data subspace, and (iv) identifies and adapts to possible changes of the query subspaces based on the theory of optimal stopping. The proposed model is decentralized, facilitating the scaling-out of such predictive analytics queries. The research significance of the model lies in that (i) it is an attractive solution when data-driven statistical techniques are undesirable or infeasible, (ii) it offers a scale-out, decentralized training solution, (iii) it is applicable to different selection query types, and (iv) it offers a performance that is superior to that of data-driven approaches.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• 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
• Journal Name: ACM Transactions on Knowledge Discovery from Data
• Publisher: Association for Computing Machinery
• ISSN: 1556-4681
• ISSN (Online): 1556-472X
• Copyright Holders: Copyright © 2017 Association for Computing Machinery
• First Published: First published in ACM Transactions on Knowledge Discovery from Data 11(4):47
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher