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

作　　者：Wenhao FAN Yuanan LIU Bihua TANG 

机构地区：[1]School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China [2]Beijing Key Laboratory of Work Safety Intelligent Monitoring Beijing University of Posts and Telecommunicationg, Beijing 100876, China

出　　处：《Science China(Information Sciences)》2016年第2期197-212,共16页中国科学（信息科学）（英文版）

基　　金：supported in part by National Natural Science Foundation of China(Grant Nos.61502050;61170275);Yang Fan Innovative&Entrepreneurial Research Team Project of Guangdong Province;Civil Aerospace Science and Technology Project and Fundamental Research Funds for the Central Universities

摘　　要：Event matching is a core in decoupled end-to-end communications, which are extensively applied to various areas. Event matching seeks the subscriptions that match a given event from a subscription set,however, this work becomes increasingly complicated in content-based multi-attribute scenarios, where events and subscriptions are formed in content, and described by multiple attributes. In addition, large-scale systems are easier to suffer from severe degradation in event matching performance. To this end, this paper presents a high-efficiency content-based multi-attribute event matching algorithm, called HEM（hybrid event matching）,which is hybridized by 2 different methods. In HEM, the matching on each single attribute（called singleattribute matching） is processed by a triangle-based matching method or a direct matching method dynamically.All single-attribute matchings are sorted via a fast near-optimal algorithm, and each of them is carried out sequentially. In this manner, the searching space of event matching shrinks gradually, so that the searching performance is boosted along with the process of event matching. Experiments are conducted to evaluate HEM comprehensively, where it is observed that HEM outperforms 3 state-of-the-art counterparts（TAMA, H-TREE and REIN） in main criteria, such as event matching time, insertion time and deletion time. Moreover, the gap of performance between HEM and the counterparts enlarges with the increase of system scale.Event matching is a core in decoupled end-to-end communications, which are extensively applied to various areas. Event matching seeks the subscriptions that match a given event from a subscription set,however, this work becomes increasingly complicated in content-based multi-attribute scenarios, where events and subscriptions are formed in content, and described by multiple attributes. In addition, large-scale systems are easier to suffer from severe degradation in event matching performance. To this end, this paper presents a high-efficiency content-based multi-attribute event matching algorithm, called HEM（hybrid event matching）,which is hybridized by 2 different methods. In HEM, the matching on each single attribute（called singleattribute matching） is processed by a triangle-based matching method or a direct matching method dynamically.All single-attribute matchings are sorted via a fast near-optimal algorithm, and each of them is carried out sequentially. In this manner, the searching space of event matching shrinks gradually, so that the searching performance is boosted along with the process of event matching. Experiments are conducted to evaluate HEM comprehensively, where it is observed that HEM outperforms 3 state-of-the-art counterparts（TAMA, H-TREE and REIN） in main criteria, such as event matching time, insertion time and deletion time. Moreover, the gap of performance between HEM and the counterparts enlarges with the increase of system scale.

关 键 词：event matching publish/subscribe data dissemination end-to-end communications interval search combinatorial optimization 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]