DKWS: A Distributed System for Keyword Search on Massive Graphs (Complete Version)

Due to the unstructuredness and the lack of schemas of graphs, such as knowledge graphs, social networks, and RDF graphs, keyword search for querying such graphs has been proposed. As graphs have become voluminous, large-scale distributed processing has attracted much interest from the database research community. While there have been several distributed systems, distributed querying techniques for keyword search are still limited. This paper proposes a novel distributed keyword search system called DKWS. First, we revisepresent a em monotonic property with keyword search algorithms that guarantees correct parallelization. Second, we present a keyword search algorithm as monotonic backward and forward search phases. Moreover, we propose new tight bounds for pruning nodes being searched. Third, we propose a em notify-push paradigm and PINE em programming model of DKWS. The notify-push paradigm allows em asynchronously exchanging the upper bounds of matches across the workers and the coordinator in DKWS. The PINE programming model naturally fits keyword search algorithms, as they have distinguished phases, to allow em preemptive searches to mitigate staleness in a distributed system. Finally, we investigate the performance and effectiveness of DKWS through experiments using real-world datasets. We find that DKWS is up to two orders of magnitude faster than related techniques, and its communication costs are 7.6 times smaller than those of other techniques.