Boolean expression matching plays an important role in many applications. However, existing solutions still show efficiency and scalability limitations. For example, existing solutions often exhibit degraded performance when applied to high-dimensional and diverse workloads, and existing algorithms rarely consider supporting concurrent matching and index updating under multicore environments. To overcome these limitations, in this article, we first design the PS-Tree data structure to efficiently index Boolean expressions in one dimension. By dividing predicates into disjoint predicate spaces, PS-Tree achieves high matching performance and good expressiveness. Based on the PS-Tree , we propose a Boolean expression matching algorithm called PSTDynamic . By dynamically adjusting the index and efficiently filtering out a large proportion of unmatching expressions, PSTDynamic achieves high matching performance under high-dimensional and diverse workloads. For multicore environment, we further extend the PSTDynamic algorithm to PSTParallel to achieve scalability with lower matching latency and higher matching throughput. We run experiments on both synthetic and real-world datasets. The experiments verify that our proposed algorithms show high efficiency and parallelism. Moreover, they also achieve fast index construction and a small memory footprint. Comprehensive experiments show that our solutions drastically outperform state-of-the-art methods.
原文链接
分享
引用
