Accelerating Parallel Evaluation of Regular Path Queries on Large Graphs by Estimating Joining Cost of Subqueries

Van-Quyet Nguyen, Van-Hau Nguyen, Huy-The Vu, Minh-Quy Nguyen, Quyet-Thang Huynh, Kyungbaek Kim

Abstract: Using Regular Path Queries (RPQs) is a common way to explore patterns in graph databases. Traditional automata-based approaches for evaluating RPQs on large graphs are restricted in the graph size and/or highly complex queries, which causes a high evaluation cost. Recently, the threshold rare label based approach applied on large graphs has been proved to be effective. Nevertheless, using rare labels in a graph provides only coarse information which could not always guarantee the minimum searching cost. Hence, the Unit-Subquery Cost Matrix (USCM) based approach has been proposed to reduce the parallel evaluation cost by estimating the searching cost of RPQs. However, the previous approach does not take the joining cost among subqueries into account. In this paper, the method of estimating joining cost of subqueries is proposed in order to accelerate the USCM based parallel evaluation of RPQs. Specifically, the proposed method is realized by estimating the result size of the subqueries. Through our experiments upon real-world datasets, it is depicted that estimating joining cost enhances USCM based approach up to around 20% in terms of response time.