Query Driven Data Subspace Mapping

Fountas, P., Papathanasaki, M., Kolomvatsos, K. and Anagnostopoulos, C. (2022) Query Driven Data Subspace Mapping. In: 18th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2022), Crete, Greece, 17-20 June 2022, ISBN 9783031083372 (doi: 10.1007/978-3-031-08337-2_41) (Early Online Publication)

Abstract

The increased use of multiple types of smart devices in several application domains, opens the pathways for the collection of humongous volumes of data. At the same time, the need for processing of only a subset of these data by applications in order to quickly conclude tasks execution and knowledge extraction, has resulted in the adoption of a very high number of queries set into distributed datasets. As a result, a significant process is the efficient response to these queries both in terms of time and the appropriate data. In this paper, we present a hierarchical query-driven clustering approach, for performing efficient data mapping in remote datasets for the management of future queries. Our work differs from other current methods in the sense that it combines a Query-Based Learning (QBL) model with a hierarchical clustering in the same methodology. The performance of the proposed model is assessed by a set of experimental scenarios while we present the relevant numerical outcomes.

• Item Type: Conference Proceedings
• Additional Information: Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 647). Print ISBN: 9783031083365.
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Kolomvatsos, Dr Kostas and Fountas, Mr Panagiotis and Anagnostopoulos, Dr Christos
• Authors: Fountas, P., Papathanasaki, M., Kolomvatsos, K., and Anagnostopoulos, C.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9783031083372
• Published Online: 10 June 2022

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