H-Tree: An Efficient Index Structurefor Event Matching in Content-BasedPublish/Subscribe Systems

Abstract

Content-based publish/subscribe systems have been employed to deal with complex distributed information flows in many applications. It is well recognized that event matching is a fundamental component of such large-scale systems. Event matching searches a space which is composed of all subscriptions. As the scale and complexity of a system grows, the efficiency of event matching becomes more critical to system performance. However, most existing methods suffer significant performance degradation when the system has large numbers of both subscriptions and their component constraints. In this paper, we present H-Tree (Hash Tree), a highly efficient index structure for event matching. H-Tree is a hash table in nature that is a combination of hash lists and hash chaining. A hash list is built up on an indexed attribute by realizing novel overlapping divisions of the attribute's value domain, providing more efficient space consumption. Multiple hash lists are then combined into a hash tree. The basic idea behind H-Tree is that matching efficiencies are improved when the search space is substantially reduced by pruning most of the subscriptions that are not matched. We have implemented H-Tree and conducted extensive experiments in different settings. Experimental results demonstrate that H-Tree has better performance than its counterparts by a large margin. In particular, the matching speed is faster by three orders of magnitude than its counterparts when the numbers of both subscriptions and their component constraints are huge.

Toward high efficiency for content-based multi-attribute event matching via hybrid methods

“…Experiments are conducted in several scenarios with multiple settings of parameters, where HEM is compared with its counterparts TAMA [9], H-TREE [10] and REIN [11]. In experiments, main criteria of event matching are evaluated, which The max number of grids on a row or a column in the arrays of the 80 triangle-based matching method in HEM η…”

“…In TAMA [9], H-TREE [10], REIN [11] and our HEM, TAMA belongs to approximate event matching algorithm, whereas, other algorithms are exact event matching algorithms. The matching result via the former may contain positive fault [9], which leads to a slightly larger result than the exact result; the algorithms in the latter can obtain exact matching result.…”

“…The operations run fast with O(1) time complexity, and cost small storage with O(1) space complexity. This is another superiority of HEM, whereas, in its counterparts (such as TAMA [9], H-TREE [10] and REIN [11]), a subscription may be stored in multiple locations, thus, insertion and deletion operations are related to the writing operations at these locations, which consume more time and occupy more storage.…”

“…The main drawbacks of TAMA are that it can only support approximate matching, and it needs extra storage since a subscriptions may be stored into multiple cells, and also it uses the counting algorithm which can not effectively wipe off unnecessary searching. H-TREE [10] is based on a tree-like index structure, which connects several specified attributes layer by layer. In each layer, the range of each attribute is partitioned into several partially overlapped cells.…”

“…Different from existing algorithms, such as TAMA [9], H-TREE [10] and REIN [11], etc., which only employ a single method to carry out event matching, conversely, HEM uses two methods adaptively to improve event matching performance via fully utilizing the different characteristics of the two methods. Moreover, the existing algorithms neglect the fact that a subscription unmatches an event as long as any one of its constraints does not satisfy the event on the corresponding attribute.…”