Estimating searching cost of regular path queries on large graphs by exploiting unit-subqueries

Regular path queries (RPQs) are widely used on a graph whose answer is a set of tuples of nodes connected by paths corresponding to a given regular expression. Traditional automata-based approach for evaluating RPQs is restricted in the explosion of graph size, which makes graph searching take high cost (i.e. memory space and response time). Recently, a cost-based optimization technique using rare labels has been proved to be effective when it is applied to large graph. However, there is still a room for improvement, because the rare labels in the graph and/or the query are coarse information which could not guarantee the minimum searching cost all the time. This is our motivation to find a new approach using fine-grained information to estimate correctly the searching cost, which helps improving the performance of RPQs evaluation. For example, by using estimated searching cost, we can decompose an RPQ into small subqueries or separate multiple RPQs into small batch of queries in an efficient way for parallelism evaluation. In this paper, we present a novel approach for estimating the searching cost of RPQs on large graphs with cost functions based on the combinations of the searching cost of unit-subqueries (i.e. every smallest possible query). We extensively evaluated our method on real-world datasets including Alibaba, Yago, Freebase as well as synthetic datasets. Experimental results show that our estimation method obtains high accuracy which is approximately 87% on average. Moreover, two comparisons with automata-based and rare label based approaches demonstrate that our approach outperforms traditional ones.