PS-tree-based efficient boolean expression matching for high-dimensional and dense workloads

Abstract

Boolean expression matching is an important function for many applications. However, existing solutions still suffer from limitations when applied to high-dimensional and dense workloads. To overcome these limitations, in this paper, we design a data structure called PS-Tree that can efficiently index subscriptions in one dimension. By dividing predicates into disjoint predicate spaces, PS-Tree achieves high matching performance and good expressiveness. Based on PS-Tree, we first propose a Boolean expression matching algorithm PSTBloom. By efficiently filtering out a large proportion of unmatching subscriptions, PSTBloom achieves high matching performance, especially for high-dimensional workloads. PSTBloom also achieves fast index construction and a small memory footprint. Compared with state-of-theart methods, comprehensive experiments show that PSTBloom reduces matching time, index construction time and memory usage by up to 84%, 78% and 94%, respectively. Although PSTBloom is effective for many workload distributions, dense workloads represent new challenges to PSTBloom and other algorithms. To effectively handle dense workloads, we further propose the PSTHash algorithm, which divides subscriptions into disjoint multidimensional predicate spaces. This organization prunes partially matching subscriptions efficiently. Comprehensive experiments on both synthetic and real-world datasets show that PSTHash improves the matching performance by up to 92% for dense workloads.

PEM: A Parallel Ensemble Matching Framework for Content-based Publish/Subscribe Systems

“…To achieve high matching performance, an efficient data structure for indexing subscriptions is necessary and critical for the matching algorithm. Classical data structures include matching trees [18] [19] [7], matching tables [20] [10] [11], binary decision diagrams [21] [22] and bloom filters [14] [13]. The underlying data structure of the matching algorithm is responsible for maintaining subscriptions (inserts, deletes and updates) and supporting event matching.…”

“…To improve matching performance, many new data structures for storing subscriptions have been proposed, such as trees [6] [7] [8] [9], tables [10] [11] [12] and bloom filters [13] [14]. These novel data structures support efficient event matching.…”

Lap: A latency‐aware parallelism framework for content‐based publish/subscribe systems

“…Examples of subscription-grouped data structures include H-tree, 16 MO-tree, 25 SM-tree, 26 Be-tree, 17 and PS-Tree. 27 When matching events, these algorithms first locate the leaf nodes that contain potential matches. Then, the subscriptions stored in these nodes are further checked to find the real matches.…”

“…The nonleaf nodes in the tree define the indexing rules for maintaining subscriptions and matching events. Examples of subscription‐grouped data structures include H‐tree, 16 MO‐tree, 25 SM‐tree, 26 Be‐tree, 17 and PS‐Tree 27 . When matching events, these algorithms first locate the leaf nodes that contain potential matches.…”

BOP: A Bitset-based Optimization Paradigm for Content-based Event Matching Algorithms (S)

“…With the continuous research and innovation of scholars, many efficient event matching algorithms have been proposed, such as PS-Tree [7], H-Tree [8], MO-Tree [9], TAMA [10], REIN [11], Siena [12], SCSL [13], HEM [14], PhSIH [15] and Comat [16]. These algorithms utilize different data structures, such as trees, tables and bloom filters, to index subscriptions to achieve high event matching speed.…”