On Continuous Top-k Similarity Joins

Da Jun Li, En Tzu Wang, Yu-Chou Tsai, Arbee L. P. Chen

2012

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 contained 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.

Paper Citation

in Harvard Style

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, pages 87-96. DOI: 10.5220/0003993200870096

in Bibtex Style

@conference{data12,
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,},
year={2012},
pages={87-96},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003993200870096},
isbn={978-989-8565-18-1},
}

in EndNote Style

TY - CONF
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