Indexing query graphs to speedup graph query processing

Abstract

Subgraph/supergraph queries although central to graph an- alytics, are costly as they entail the NP-Complete problem of subgraph isomorphism. We present a fresh solution, the novel principle of which is to acquire and utilize knowledge from the results of previously executed queries. Our ap- proach, iGQ, encompasses two component subindexes to identify if a new query is a subgraph/supergraph of pre- viously executed queries and stores related key informa- tion. iGQ comes with novel query processing and index space management algorithms, including graph replacement policies. The end result is a system that leads to signifi- cant reduction in the number of required subgraph isomor- phism tests and speedups in query processing time. iGQ can be incorporated into any sub/supergraph query processing method and help improve performance. In fact, it is the only contribution that can speedup significantly both subgraph and supergraph query processing. We establish the princi- ples of iGQ and formally prove its correctness. We have im- plemented iGQ and have incorporated it within three popu- lar recent state of the art index-based graph query process- ing solutions. We evaluated its performance using real-world and synthetic graph datasets with different characteristics, and a number of query workloads, showcasing its benefits.