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计算机系统应用:2020,29(8):72-79
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基于扩展ASP的RDF流处理系统
(1.中国科学院大学, 北京 100049;2.中国科学院 沈阳计算技术研究所, 沈阳 110168)
RDF Stream Processing System Based on Extended ASP
(1.University of Chinese Academy of Sciences, Beijing 100049, China;2.Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China)
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投稿时间:2020-01-09    修订日期:2020-02-08
中文摘要: 对传感器产生的语义数据流执行复杂推理的能力, 最近已成为语义网社区中的重要研究领域, 而目前大多数RDF流处理系统是以SPARQL (W3C标准RDF查询语言)为基础实现的, 但这些引擎在捕获复杂的用户需求和处理复杂的推理任务方面存在局限性. 针对此问题, 本文结合并扩展了回答集编程(Answer Set Programing, ASP)技术用于对RDF流进行连续的处理. 为了验证本方法的有效性, 首先以智能家居本体为实验对象, 并分析传感器设备间的共有特性及复杂事件以构建本体库; 然后基于本体库产生实例对象, 并通过中间件产生RDF数据流; 接下来通过扩展ASP, 充分利用其表达和推理能力以减少推理时间, 并设计了RDF 流的窗口划分策略等, 然后根据用户的请求, 选择性地进行静态知识库加载等; 最后通过实验与Sparkwave和Laser进行对比, 证明了该方法在延迟和内存上的性能优势.
中文关键词: RDF流  复杂事件处理  查询推理  ASP  SPARQL
Abstract:The ability to perform complex reasoning on semantic data streams generated by sensors has recently become an important research area in the Semantic Web community. Currently, most RDF stream processing systems are implemented based on SPARQL (W3C Standard Protocol and RDF Query Language), but these engines have limitations in capturing complex user requirements and processing complex reasoning tasks. In response to this problem, this study combines and extends Answer Set Programming (ASP) technology for continuous processing of RDF streams. In order to verify the effectiveness of this method, we firstly take the smart home ontology as the experimental object, and analyze the common characteristics and complex events between the sensor devices to build the ontology library; then generate instance objects based on the ontology library and generate RDF data stream through middleware. Next, through extending ASP, making full use of its expression, and reasoning capabilities and reducing the reasoning time, a window partitioning strategy for the RDF stream in this method is designed. The static knowledge base is selectively loaded according to the user’s request. Finally, the comparison with Sparkwave and Laser through experiments proves the performance advantage of this method in terms of latency and memory.
文章编号:7588     中图分类号:    文献标志码:
基金项目:
引用文本:
林维淦,刘峰,于碧辉.基于扩展ASP的RDF流处理系统.计算机系统应用,2020,29(8):72-79
LIN Wei-Gan,LIU Feng,YU Bi-Hui.RDF Stream Processing System Based on Extended ASP.COMPUTER SYSTEMS APPLICATIONS,2020,29(8):72-79

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