Hybrid Algorithms for Subgraph Pattern Queries in Graph Databases

Katsarou, F., Ntarmos, N. and Triantafillou, P. (2018) Hybrid Algorithms for Subgraph Pattern Queries in Graph Databases. In: 2017 IEEE International Conference on Big Data (IEEE BigData 2017), Boston, MA, USA, 11-14 Dec 2017, pp. 656-665. ISBN 9781538627150 (doi:10.1109/BigData.2017.8257981)

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Abstract

Numerous methods have been proposed over the years for subgraph query processing, as it is central to graph analytics. Existing work is fragmented into two major categories. Methods in the filter-then-verify (FTV) category first construct an index of the DB graphs. Given a query, the index is used to filter out graphs that cannot contain the query. On the remaining graphs, a subgraph isomorphism algorithm is applied to verify whether each graph indeed contains the query. A second category of algorithms is mainly concerned with optimizing the Subgraph Isomorphism (SI) testing process (an NP-Complete problem) in order to find all occurrences of the query within each DB graph, also known as the matching problem. The current research trend is to totally dismiss FTV methods, because SI methods have been shown to enjoy much shorter query execution times and because of the alleged high costs of managing the DB graph index in FTV methods. Thus, a number of new SI methods are being proposed annually. In the current work, we initially study the performance of the latest SI algorithms over datasets consisting of a large number of graphs. With our study, we evaluate the algorithms' performance and we provide comparison details with former studies. As a second step, we combine the powerful filtering of a top-performing FTV method, with the various SI methods, which leads to the best practice conclusion that SI and FTV shouldn't be thought of as disjoint types of solutions, as their union achieves better results than any one of them individually. Specifically, we experimentally analyze and quantify the (positive) impact of including the essence of indexed FTV methods within SI methods, showing that query processing times can be significantly improved at modest additional memory costs. We show that these results hold over a variety of well-known SI methods and across several real and synthetic datasets. As such, hybrids of the type reveal a missing opportunity and a blind spot in related literature and trends.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Katsarou, Foteini and Triantafillou, Professor Peter and Ntarmos, Dr Nikolaos
Authors: Katsarou, F., Ntarmos, N., and Triantafillou, P.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781538627150
Copyright Holders:Copyright © 2018 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
736751PRIMES: Personalised Recommendations and Internationalisation for MOOCs in European SchoolsNikolaos NtarmosEuropean Commission (EC)KA201-024631SCHOOL OF COMPUTING SCIENCE