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Authors: Da Jun Li 1 ; En Tzu Wang 2 ; Yu-Chou Tsai 3 and Arbee L. P. Chen 4

Affiliations: 1 National Tsing Hua University, Taiwan ; 2 Industrial Technology Research Institute, Taiwan ; 3 Institute for Information Industry, Taiwan ; 4 National Chengchi University, Taiwan

ISBN: 978-989-8565-18-1

ISSN: 2184-285X

Keyword(s): Data Stream, Similarity Join, Continuous Query, Top-K Query.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Data Engineering ; Data Management and Quality ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Modeling and Managing Large Data Systems ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: Given a similarity function and a threshold  within a range of [0, 1], a similarity join query between two sets of records returns pairs of records from the two sets, which have similarity values exceeding or equaling . Similarity joins have received much research attention since it is a fundamental operation used in a wide range of applications such as duplicate detection, data integration, and pattern recognition. Recently, a variant of similarity joins is proposed to avoid the need to set the threshold , i.e. top-k similarity joins. Since data in many applications are generated as a form of continuous data streams, in this paper, we make the first attempt to solve the problem of top-k similarity joins considering a dynamic environment involving a data stream, named continuous top-k similarity joins. Given a set of records as the query, we continuously output the top-k pairs of records, ranked by their similarity values, for the query and the most recent data, i.e. the data conta ined in the sliding window of a monitored data stream. Two algorithms are proposed to solve this problem. The first one extends an existing approach for static datasets to find the top-k pairs regarding the query and the newly arrived data and then keep the obtained pairs in a candidate result set. As a result, the top-k pairs can be found from the candidate result set. In the other algorithm, the records in the query are preprocessed to be indexed using a novel data structure. By this structure, the data in the monitored stream can be compared with all records in the query at one time, substantially reducing the processing time of finding the top-k results. A series of experiments are performed to evaluate the two proposed algorithms and the experiment results demonstrate that the algorithm with preprocessing outperforms the other algorithm extended from an existing approach for a static environment. (More)

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Paper citation in several formats:
Jun Li, D.; Tzu Wang, E.; Tsai, Y. and L. P. Chen, A. (2012). On Continuous Top-k Similarity Joins.In Proceedings of the International Conference on Data Technologies and Applications - Volume 1: DATA, ISBN 978-989-8565-18-1, ISSN 2184-285X, pages 87-96. DOI: 10.5220/0003993200870096

author={Da Jun Li. and En Tzu Wang. and Yu{-}Chou Tsai. and Arbee L. P. Chen.},
title={On Continuous Top-k Similarity Joins},
booktitle={Proceedings of the International Conference on Data Technologies and Applications - Volume 1: DATA,},


JO - Proceedings of the International Conference on Data Technologies and Applications - Volume 1: DATA,
TI - On Continuous Top-k Similarity Joins
SN - 978-989-8565-18-1
AU - Jun Li, D.
AU - Tzu Wang, E.
AU - Tsai, Y.
AU - L. P. Chen, A.
PY - 2012
SP - 87
EP - 96
DO - 10.5220/0003993200870096

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