Computer Science ›› 2019, Vol. 46 ›› Issue (2): 35-41.doi: 10.11896/j.issn.1002-137X.2019.02.006

• Big Data & Data Science • Previous Articles     Next Articles

Multi-keyword Streaming Parallel Retrieval Algorithm Based on Urban Security Knowledge Graph

GUANJian, WANG Jing-bin, BIAN Qian-hong   

  1. College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350116,China
  • Received:2018-07-13 Online:2019-02-25 Published:2019-02-25

Abstract: With the popularization and construction of the concept of smart city security in China,and the deep application of big data in the construction of smart city security,higher requirements on the processing response speed of keyword retrieval are needed.Aiming at this problem,this paper proposed a streaming multi-keyword parallel retrieval algorithm based on the urban security knowledge graph (MKPRASKG).This algorithm can construct a query subgraph set based on the entities of knowledge graph through the construction,pruning and fusion operation of the associated class graphs based on the query keywords input by the user in real time.And then combined with the scoring function,the high-scoring query subgraph is used as a guide,and the parallel search is performed in the knowledge graph instance data,and finally the Top-k query results are returned.Experimental results show that this algorithm has great advantages in terms of real-time search,response time,search effect and scalability.

Key words: Knowledge graph, Multi-keyword search, Real time, Streaming

CLC Number: 

  • TP319
[1]CHEN H S,HAN Z,DENG S N.Analysis and Research of Big Data Security in Smart Cities[J].Information Network Security,2015(7):1-6.(in Chinese)
陈红松,韩至,邓淑宁.智慧城市中大数据安全分析与研究[J].信息网络安全,2015(7):1-6.
[2]WAN S,LU J,FAN P,et al.To Smart City:Public Safety Network Design for Emergency[J].IEEE Access,2018,6(99):1451-1460.
[3]WANG Y Z,JIN X L,CHENG X Q.Network Big Data:Current Status and Prospects[J].Chinese Journal of Computers,2013,36(6):1125-1138.(in Chinese)
王元卓,靳小龙,程学旗.网络大数据:现状与展望[J].计算机学报,2013,36(6):1125-1138.
[4]MENG X F,CI X.Big Data Management:Concepts,Technolo- gies and Challenges [J].Journal of Computer Research and Development,2013,50(1):146-169.(in Chinese)
孟小峰,慈祥.大数据管理:概念、技术与挑战[J].计算机研究与发展,2013,50(1):146-169.
[5]BUXTON B,GOLDSTON D,DOCTOROW C,et al.Big data: science in the petabyte era[J].Nature,2008,455(7209):8-9.
[6]CHEN C.Streaming big data real-time processing technology,platform and application [J].Big Data,2017,3 (4):1-8.(in Chinese)
陈纯.流式大数据实时处理技术、平台及应用[J].大数据,2017,3(4):1-8.
[7]DUAN Z Y.Research on load balancing and fault tolerance mechanism of big data streaming system [D].Beijing:North China Electric Power University,2017.(in Chinese)
段泽源.大数据流式处理系统负载均衡与容错机制的研究[D].北京:华北电力大学,2017.
[8]HIRIYANNAIAH S,SIDDESH G M,SRINIVASA K G,et al.Real-Time Streaming Data Analysis Using a Three-Way Classification Method for Sentimental Analysis[J].International Journal of Information Technology & Web Engineering,2018,13(3):99-111.
[9]BORTHAKUR D,GRAY J,SARMA J S,et al.Apache hadoop goes realtime at Facebook[C]∥ACM SIGMOD International Conference on Management of Data(SIGMOD 2011).Athens,Greece,DBLP,2011:1071-1080.
[10]KEETON K,PATTERSON D A,HE Y Q,et al.Performance characterization of a Quad Pentium Pro SMP using OLTP workloads:Technical Report UCB//CSD-98-1001[R].University of California at Berkeley,Computer Science Division,1998:15-26.
[11]CHEN H,MIGLIAVACCA M.StreamDB:A Unified Data Ma- nagement System for Service-Based Cloud Application[C]∥IEEE International Conference on Services Computing.IEEE Computer Society,2018:169-176.
[12]GUI H,FENG Y C,LI Y K.Research on data management system oriented to stream data[J].Journal of Computer Applications,2005,22(1):88-90.(in Chinese)
桂浩,冯玉才,李又奎.面向流数据的数据管理系统的研究[J].计算机应用研究,2005,22(1):88-90.
[13]CARNEY D,ETINTEMEL U,CHERNIACK M,et al.Monitoring streams:a new class of data management applications[C]∥Proc.International Conference on Very Large Data Bases.Hong Kong,China,2002:215-226.
[14]CHANDRASEKARAN S,COOPER O,DESHPANDE A,et al.TelegraphCQ:continuous dataflow processing[C]∥ACM SIGMOD International Conference on Management of Data.ACM,2003:668-668.
[15]HASSAN M,BANSAL S K.Semantic Data Querying over NoSQL Databases with Apache Spark[C]∥IEEE International Conference on Information Reuse and Integration.IEEE Computer Society,2018:364-371.
[16]HOU R J,FANG J,ZHANG J J.A data query method for real-time write protection of streaming data[J].Journal of Computer Applications,2014,31(9):2736-2740.(in Chinese)
侯荣军,房俊,张建静.一种流数据实时写入保障下的数据查询方法[J].计算机应用研究,2014,31(9):2736-2740.
[17]VIRGILIO R D,MACCIONI A.Distributed Keyword Search over RDF via MapReduce[C]∥European Semantic Web Confe-rence.Springer,Cham,2014:208-223.
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