Multi-query optimization for subgraph isomorphism search

Abstract

Existing work on subgraph isomorphism search mainly focuses on a-query-at-a-time approaches: optimizing and answering each query separately. When multiple queries arrive at the same time, sequential processing is not always the most efficient. In this paper, we study multi-query optimization for subgraph isomorphism search. We first propose a novel method for efficiently detecting useful common subgraphs and a data structure to organize them. Then we propose a heuristic algorithm based on the data structure to compute a query execution order so that cached intermediate results can be effectively utilized. To balance memory usage and the time for cached results retrieval, we present a novel structure for caching the intermediate results. We provide strategies to revise existing single-query subgraph isomorphism algorithms to seamlessly utilize the cached results, which leads to significant performance improvement. Extensive experiments verified the effectiveness of our solution.

A generic database indexing framework for large-scale geographic knowledge graphs

“…Query Generation. The query graphs are generated by exploiting the subgraph generator used in the existing paper [13]. A core vertex is randomly picked up from the original data graph and a subgraph around this core vertex is generated by random walk.…”

A Survey on Distributed Graph Pattern Matching in Massive Graphs

“…Others create tree/graph indices for storing the candidates' sets like GraphQL [38], TurboIso [37], BoostIso [63], CFL-Match [4], TurboFlux [45], DAF [36] and VC [80], while some construct indices based on the data graph and use them to assist the embeddings enumeration; GADDI [103] and SPath [105]. Finally, MQO-iso [64] constructs a DAG based on the relationships between multiple queries to guide subgraph isomorphism search.…”

“…Furthermore, [64] proposes a multi-query optimization plan (MQO-iso) to reduce the processing time of multiple queries. The input query set is processed to detect the common subgraphs, then a DAG is built based on the isomorphic relationships between the queries such that there is an edge from to ′ if is subgraph isomorphic to ′ .…”

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“…It is widely used in a number of applications, for example, web document classification, software plagiarism detection, and protein structure detection [1][2][3].…”