Leveraging Pattern Mining Techniques for Efficient Keyword Search on Data Graphs

Document Type

Conference Proceeding

Publication Date

1-1-2020

Abstract

Graphs model complex relationships among objects in a variety of web applications. Keyword search is a promising method for extraction of data from data graphs and exploration. However, keyword search faces the so called performance scalability problem which hinders its widespread use on data graphs. In this paper, we address the performance scalability problem by leveraging techniques developed for graph pattern mining. We focus on avoiding the generation of redundant intermediate results when the keyword queries are evaluated. We define a canonical form for the isomorphic representations of the intermediate results and we show how it can be checked incrementally and efficiently. We devise rules that prune the search space without sacrificing completeness and we integrate them in a query evaluation algorithm. Our experimental results show that our approach outperforms previous ones by orders of magnitude and displays smooth scalability.

Identifier

85080902508 (Scopus)

ISBN

[9789811532801]

Publication Title

Communications in Computer and Information Science

External Full Text Location

https://doi.org/10.1007/978-981-15-3281-8_10

e-ISSN

18650937

ISSN

18650929

First Page

98

Last Page

114

Volume

1155 CCIS

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