Efficiently Computing Homomorphic Matches of Hybrid Pattern Queries on Large Graphs

Document Type

Conference Proceeding

Publication Date

1-1-2019

Abstract

In this paper, we address the problem of efficiently finding homomorphic matches for hybrid patterns over large data graphs. Finding matches for patterns in data graphs is of fundamental importance for graph analytics. In hybrid patterns, each edge may correspond either to an edge or a path in the data graph, thus allowing for higher expressiveness and flexibility in query formulation. We introduce the concept of answer graph to compactly represent the query results and exploit computation sharing. We design a holistic bottom-up algorithm called GPM, which greatly reduces the number of intermediate results, leading to significant performance gains. GPM directly processes child constraints in the given query instead of resorting to a post-processing procedure. An extensive experimental evaluation using both real and synthetic datasets shows that our methods evaluate hybrid patterns up to several orders of magnitude faster than existing algorithms and exhibit much better scalability.

Identifier

85077117619 (Scopus)

ISBN

[9783030275198]

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

External Full Text Location

https://doi.org/10.1007/978-3-030-27520-4_20

e-ISSN

16113349

ISSN

03029743

First Page

279

Last Page

295

Volume

11708 LNCS

Grant

61872276

Fund Ref

National Natural Science Foundation of China

This document is currently not available here.

Share

COinS