Abstract
There is a growing need to perform real-time analytics on dynamic graphs in order to deliver the values of big data to users. An important problem from such applications is continuously identifying and monitoring critical patterns when fine-grained updates at a high velocity occur on the graphs. A lot of efforts have been made to develop practical solutions for these problems. Despite the efforts, existing algorithms showed limited running time and scalability in dealing with large and/or many graphs. In this paper, we study the problem of continuous multi-query optimization for subgraph matching over dynamic graph data. (1) We propose annotated query graph, which is obtained by merging the multi-queries into one. (2) Based on the annotated query, we employ a concise auxiliary data structure to represent partial solutions in a compact form. (3) In addition, we propose an efficient maintenance strategy to detect the affected queries for each update and report corresponding matches in one pass. (4) Extensive experiments over real-life and synthetic datasets verify the effectiveness and efficiency of our approach and confirm a two orders of magnitude improvement of the proposed solution.
Highlights
O Dynamic graphs emerge in different domains, such as financial transaction network, mobile communication netC work, data center network [20,21,22], uncertain network [2], etc
Annotated query graph R Different from the work proposed in [30] that decomposes queries into covering paths and handles updates by finding affected paths, we provide a novel concept of annotated query graph, namely, AQG, which merges all queries
We evaluated the performance of IncMQO against its competitors from the aspect of processing time and storage cost on three datasets: SNB1M, NYC and BioGRID with a default updates stream | g| = 15%|G|
Summary
O Dynamic graphs emerge in different domains, such as financial transaction network, mobile communication netC work, data center network [20,21,22], uncertain network [2], etc. Zervakis et al [30] first propose a continuous multi-query process engine, namely, TRIC, on the dynamic graph. It decomposes the query graphs into minimum covering paths and constructs an index. In order to avoid executing subgraph pattern matching repeatedly whenever some edges expire or some new edges arrive, we need to construct an auxiliary data structure to record some intermediate query. Note that data-centric representation of intermediate results is claimed to have the best performance in storage cost [15] It maintains candidate query vertices for each data vertex using a graph structure such that a data. – We propose an efficient continuous multi-query matching system, IncMQO, to resolve the problems of existing methods. The experiment results show that our solution can achieve up to two orders of magnitude improvement in query processing time against the sequential processing strategy
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