Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (4): 762-774.doi: 10.1007/s11390-019-1941-9

Special Issue: Data Management and Data Mining

• Special Section on Spatio-Temporal Big Data Analytics • Previous Articles     Next Articles

A United Framework for Large-Scale Resource Description Framework Stream Processing

Hong Fang1, Bo Zhao2,3, Xiao-Wang Zhang2,3,*, Member, CCF, Xuan-Xing Yang2,3   

  1. 1 College of Arts and Sciences, Shanghai Polytechnic University, Shanghai 201209, China;
    2 College of Intelligence and Computing, Tianjin University, Tianjin 300350, China;
    3 Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin 300350, China
  • Received:2019-01-15 Revised:2019-05-09 Online:2019-07-11 Published:2019-07-11
  • Contact: Xiao-Wang Zhang E-mail:xiaowangzhang@tju.edu.cn
  • Supported by:
    This paper is supported by the National Key Research and Development Program of China under Grant No. 2017YFC0908401, and the National Natural Science Foundation of China under Grant No. 61672377. Xiao-Wang Zhang is supported by the program of Peiyang Young Scholars of China under Grant No. 2019XRX-0032.

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Resource description framework (RDF) stream is useful to model spatio-temporal data. In this paper, we propose a framework for large-scale RDF stream processing, LRSP, to process general continuous queries over large-scale RDF streams. Firstly, we propose a formalization (named CT-SPARQL) to represent the general continuous queries in a unified, unambiguous way. Secondly, based on our formalization we propose LRSP to process continuous queries in a common white-box way by separating RDF stream processing, query parsing, and query execution. Finally, we implement and evaluate LRSP with those popular continuous query engines on some benchmark datasets and real-world datasets. Due to the architecture of LRSP, many efficient query engines (including centralized and distributed engines) for RDF can be directly employed to process continuous queries. The experimental results show that LRSP has a higher performance, specially, in processing large-scale real-world data.

Key words: resource description framework(RDF)stream; continuous query; united framework; stream processing; largescale RDF stream processing(LRSP);

[1] Barbieri D F, Braga D, Ceri S, Valle E D, Grossniklaus M. Querying RDF streams with C-SPARQL. ACM SIGMOD Record, 2010, 39(1):20-26.
[2] Le-Phuoc D, Dao-Tran M, Parreira J X, Hauswirth M. A native and adaptive approach for unified processing of linked streams and linked data. In Proc. the 10th Int. Semantic Web Conference, October 2011, pp.370-388.
[3] Anicic D, Fodor P, Rudolph S, Stojanovic N. EP-SPARQL:A unified language for event processing and stream reasoning. In Proc. the 20th Int. Conference on World Wide Web, March 2011, pp.635-644.
[4] Zou L, Özsu M T, Chen L, Shen X, Huang R, Zhao D. gStore:A graph-based SPARQL query engine. The VLDB Journal, 2014, 23(4):565-590.
[5] Neumann T, Weikum G. The RDF-3X engine for scalable management of RDF data. The VLDB Journal, 2010, 19(1):91-113.
[6] Peng P, Zou L, Özsu M T, Chen L, Zhao D. Processing SPARQL queries over distributed RDF graphs. The VLDB Journal, 2016, 25(2):243-268.
[7] Gurajada S, Seufert S, Miliaraki I, Theobald M. TriAD:A distributed shared-nothing RDF engine based on asynchronous message passing. In Proc. the 2014 ACM SIGMOD Int. Conference on Management of Data, June 2014, pp.289-300.
[8] Li Q, Zhang X, Feng Z. PRSP:A plugin-based framework for RDF stream processing. In Proc. the 26th Int. Conference on World Wide Web Companion, April 2017, pp.815-816.
[9] Alessandro M, Gianpaolo C. Processing flows of information:From data stream to complex event processing. In Proc. the 5th ACM Int. Conference on Distributed EventBased Systems, July 2011, pp.359-360.
[10] Kolchin M, Wetz P, Kiesling E, Tjoa A M. YABench:A comprehensive framework for RDF stream processor correctness and performance assessment. In Proc. the 16th International Conference on Web Engineering, June 2016, pp.280-298.
[11] Arasu A, Babu S, Widom J. The CQL continuous query language:Semantic foundations and query execution. The VLDB Journal, 2006, 15(2):121-142.
[12] Carroll J J, Dickinson I, Dollin C, Reynolds D, Seaborne A, Wilkinson K. Jena:Implementing the semantic web recommendations. In Proc. the 13th Int. Conference on World Wide Web-Alternate Track Papers & Posters, May 2004, pp.74-83.
[13] Ren X, Curé O. Strider:A hybrid adaptive distributed RDF stream processing engine. In Proc. the 16th Int. Semantic Web Conference, October 2017, pp.559-576.
[14] Dell'Aglio D, Valle E D, Calbimonte J P, Corcho Ó. RSP-QL semantics:A unifying query model to explain heterogeneity of RDF stream processing systems. Int. Journal on Semantic Web and Information Systems, 2014, 10(4):17-44.
[15] Dell'Aglio D, Calbimonte J P, Valle E D, Corcho Ó. To-wards a unified language for RDF stream query processing. In Proc. the 12th European Semantic Web Conference, May 2015, pp.353-363.
[16] Brandt S, Kalayci E G, Ryzhikov V, Xiao G, Zakharyaschev M. Querying log data with temporal logic. Journal of Artificial Intelligence Research, 2018, 62:829-877.
[17] Li L, Kim J, Xu J, Zhou X. Time-dependent route scheduling on road networks. ACM SIGSPATIAL Special, 2018, 10(1):10-14.
[18] Qian Z, Xu J, Zheng K, Zhao P, Zhou X. Semantic-aware top-k spatial keyword queries. World Wide Web:Internet and Web Information Systems, 2018, 21(3):573-594.
[19] Lanti D, Xiao G, Calvanese D. VIG:Data scaling for OBDA benchmarks. Semantic Web, 2019, 10(2):413-433.
[20] Zhao B. Research on adaptive RDF stream processing architecture[Master Thesis]. College of Intelligence and Computing, Tianjin University, 2018. (in Chinese)
[21] Li J, Liu C, Yu J X, Chen Y, Sellis T, Culpepper J S. Personalized influential topic search via social network summarization. IEEE Transactions on Konwledge and Data Engineering, 2016, 28(7):1820-1834.
[22] Li J, Sellis T, Culpepper J S, He Z, Liu C, Wang J. Geosocial influence spanning maximization. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(8):1653-1666.
[23] Li J, Wang X, Deng K, Yang X, Sellis T, Yu J X. Most influential community search over large social networks. In Proc. the 33rd IEEE Int. Conference on Data Engineering, April 2017, pp.871-882.
[24] Li J, Liu C, Islam M S. Keyword-based correlated network computation over large social media. In Proc. the 30th IEEE Int. Conference on Data Engineering, March 2014, pp.268-279.
[25] Li J, Cai T, Mian A, Li R, Sellis T, Yu J X. Holistic influence maximization for targeted advertisements in spatial social networks. In Proc. the 34th IEEE Int. Conference on Data Engineering, April 2018, pp.1340-1343.
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[1] Min Yinghua;. Easy Test Generation PLAs[J]. , 1987, 2(1): 72 -80 .
[2] Cheng Jinsong;. A Parallel Algorithm for Finding Roots of a Complex Polynomial[J]. , 1990, 5(1): 71 -81 .
[3] Yu Shiwen;. Application of Grammatical Parsing Technique in Chinese Input[J]. , 1990, 5(4): 312 -318 .
[4] Yu Huiqun; Sun Yongqiang;. Hybridity in Embedded Computing Systems[J]. , 1996, 11(1): 90 -96 .
[5] Sun Ninghui; Liu Wenzhuo; Liu Hong; Wang Chuanbao; Lu Xuelin; Zhang Hao;. Dawning-1000 PROOS Distributed Operating System[J]. , 1997, 12(2): 160 -166 .
[6] Shen Li;. Fuzzy Logic Control ASIC Chip[J]. , 1997, 12(3): 263 -270 .
[7] Jing Wang, Li-Yong Zhang, Yan-Bo Han. Client-Centric Adaptive Scheduling of Service-Oriented Applications[J]. , 2006, 21(4): 537 -546 .
[8] Xiao-Dong Li, Wen-Jian Luo, and Xin Yao. Preface[J]. , 2008, 23(1): 1 .
[9] Grigorios Loukides and Jian-Hua Shao. An Efficient Clustering Algorithm for k-Anonymisation[J]. , 2008, 23(2): 188 -202 .
[10] Xiao-Min Zhu and Pei-Zhong Lu. Multi-Dimensional Scheduling for Real-Time Tasks on Heterogeneous Clusters[J]. , 2009, 24(3): 434 -446 .

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