PRSPR: An Adaptive Framework for Massive RDF Stream Reasoning

  • Guozheng Rao
  • Bo Zhao
  • Xiaowang ZhangEmail author
  • Zhiyong Feng
  • Guohui Xiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10987)


In this paper, we propose a plugin-based framework for massive RDF stream reasoning to support complicated tasks on RDF stream in an adaptive and flexible way. Within this framework, the problem of RDF stream reasoning can be equivalently reduced to the combination problem of SPARQL querying and rule-based reasoning. Take advantage of the plug-in method, we can apply various off-the-shelf SPARQL query engines and rule-based reasoners in a simple way. Moreover, to efficiently support real-time reasoning on massive RDF stream, we develop a multi-threaded batch processing approach to manage resources and an adaptive reasoning plan for dynamically managing inference rules in the stream reasoning. Finally, our experiments evaluate on dataset built on the benchmark LUBM and DBpedia. The experimental results show that our framework is effective and efficient.



We would like to thank Qiong Li for constructive comments. This work is supported by the National Natural Science Foundation of China (61373165, 61672377), the National Key R&D Program of China (2016YFB1000603, 2017YFC0908401), and the Key Technology R&D Program of Tianjin (16YFZCGX00210).


  1. 1.
    Barbieri, D.F., Braga, D., Ceri, S., Della Valle, E., Grossniklaus, M.: Querying RDF streams with C-SPARQL. SIGMOD REC. 39(1), 20–26 (2010)CrossRefGoogle Scholar
  2. 2.
    Liu, C., Qi, G., Wang, H., Yu, Y.: Large scale fuzzy \({pD}\)* reasoning using MapReduce. In: Aroyo, L., et al. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 405–420. Springer, Heidelberg (2011). Scholar
  3. 3.
    Guo, Y., Pan, Z., Heflin, J.: LUBM: a benchmark for OWL knowledge base systems. J. Web Sem. 3(2–3), 158–182 (2005)CrossRefGoogle Scholar
  4. 4.
    Gu, R., Wang, S., Wang, F., Yuan, C., Huang, Y.: Cichlid: efficient large scale RDFS/OWL reasoning with Spark. In: Proceedings of IPDPS 2015, pp. 700–709 (2015)Google Scholar
  5. 5.
    Gurajada, S., Seufert, S., Miliaraki, I., Theobald, M.X.: TriAD: A distributed shared-nothing RDF engine based on asynchronous message passing. In: Proceedings of SIGMOD 2014, pp. 289–300 (2014)Google Scholar
  6. 6.
    Antoniou, G., van Harmelen, F.: A Semantic Web Primer. The MIT Press, Cambridge (2004)Google Scholar
  7. 7.
    Li, Q., Zhang, X., Feng, Z.: PRSP: a plugin-based framework for RDF stream processing. In: Proceedings of WWW 2017, pp. 815–816 (2017)Google Scholar
  8. 8.
    Li, Q., Zhang, X., Feng, Z., Xiao, G.: An adaptive framework for RDF stream reasoning. In: Proceedings of ISWC 2017 (2017)Google Scholar
  9. 9.
    Liu, Z., Feng, Z., Zhang, X., Wang, X., Rao, G.: RORS: enhanced rule-based OWL reasoning on spark. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds.) APWeb 2016. LNCS, vol. 9932, pp. 444–448. Springer, Cham (2016). Scholar
  10. 10.
    Liu, C., Urbani, J., Qi, G.: Efficient RDF stream reasoning with graphics processingunits (GPUs). In: Proceedings of WWW 2014, pp. 343–344 (2014)Google Scholar
  11. 11.
    Margara, A., Cugola, G.: Processing flows of information: from data stream to complex event processing. In: Proceedings of DEBS 2011, pp. 359–360 (2011)Google Scholar
  12. 12.
    Neumann, T., Weikum, G.: The RDF-3X engine for scalable management of RDF data. VLDB J. 19(1), 91–113 (2010)CrossRefGoogle Scholar
  13. 13.
    Ren, X., Curé, O., Ke, L., Lhez, J., Belabbess, B., Randriamalala, T., Zheng, Y.: Strider: an adaptive, inference-enabled distributed RDF stream processing engine. PVLDB 10(12), 1905–1908 (2017)Google Scholar
  14. 14.
    Urbani, J.: RDFS/OWL reasoning using the MapReduce framework. Master’s thesis, Vrije Universiteit - Faculty of Sciences, Department of Computer Science (2009)Google Scholar
  15. 15.
    Zhang, Y., Duc, P.M., Corcho, O., Calbimonte, J.-P.: SRBench: a streaming RDF/SPARQL benchmark. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012. LNCS, vol. 7649, pp. 641–657. Springer, Heidelberg (2012). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Guozheng Rao
    • 1
    • 3
  • Bo Zhao
    • 1
    • 3
  • Xiaowang Zhang
    • 1
    • 3
    Email author
  • Zhiyong Feng
    • 2
    • 3
  • Guohui Xiao
    • 4
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.School of Computer SoftwareTianjin UniversityTianjinChina
  3. 3.Tianjin Key Laboratory of Cognitive Computing and ApplicationTianjinChina
  4. 4.Faculty of Computer ScienceFree University of Bozen-BolzanoBolzanoItaly

Personalised recommendations