Query Optimization for Dynamic Graphs

TL;DR

This paper introduces an incremental query processing algorithm for dynamic graphs that uses query decomposition and statistical metrics to improve efficiency, achieving significant speedups over existing methods.

Contribution

It presents a novel approach combining query decomposition with distributional statistics and a Lazy Search strategy for efficient continuous query processing on dynamic graphs.

Findings

Achieved 10-100x speedups over competing approaches.

Demonstrated effectiveness on real and synthetic datasets.

Introduced the Relative Selectivity metric for strategy selection.

Abstract

Given a query graph that represents a pattern of interest, the emerging pattern detection problem can be viewed as a continuous query problem on a dynamic graph. We present an incremental algorithm for continuous query processing on dynamic graphs. The algorithm is based on the concept of query decomposition; we decompose a query graph into smaller subgraphs and assemble the result of sub-queries to find complete matches with the specified query. The novelty of our work lies in using the subgraph distributional statistics collected from the dynamic graph to generate the decomposition. We introduce a "Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named "Relative Selectivity" that is used to select between different query decomposition strategies. Our experiments performed on real online news, network traffic stream and a synthetic social network benchmark demonstrate 10-100x speedups over competing approaches.