Fast and Accurate Optimizer for Query Processing over Knowledge Graphs

Abstract

This paper presents Gpl, a fast and accurate optimizer for query processing over knowledge graphs. Gpl is novel in three ways. First, Gpl proposes a type-centric approach to enhance the accuracy of cardinality estimation prominently, which naturally embeds the correlation of multiple query conditions into the existing type system of knowledge graphs. Second, to predict execution time accurately, Gpl constructs a specialized cost model for graph exploration scheme and tunes the coefficients with target hardware platform and graph data. Third, Gpl further uses a budget-aware strategy for plan enumeration with a greedy heuristic to boost the overall performance (i.e., optimization time and execution time) for various workloads. Evaluations with representative knowledge graphs and query benchmarks show that Gpl can select optimal plans for 33 of 39 queries and only incurs less than 5% slowdown on average compared to optimal results. In contrast, the state-of-the-art optimizer and manually tuned results will cause 100% and 36% slowdown, respectively.