Abstract
In this paper, we propose a novel framework for RDF stream processing named PRSP. Within this framework, the evaluation of C-SPARQL queries on RDF streams can be reduced to the evaluation of SPARQL queries on RDF graphs. We prove that the reduction is sound and complete. With PRSP, we implement several engines to support C-SPARQL queries by employing current SPARQL query engines such as Jena, gStore, and RDF-3X. The experiments show that PRSP can still maintain the high performance by applying those engines in RDF stream processing, although there are some slight differences among them. Moreover, taking advantage of PRSP, we can process large-scale RDF streams in a distributed context via distributed SPARQL engines, such as gStoreD and TriAD. Besides, we can evaluate the performance and correctness of existing SPARQL query engines in processing RDF streams in a unified way, which amends the evaluation of them ranging from static RDF data to dynamic RDF data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boncz, P.A., Kersten, M.L., Manegold, S.: Breaking the memory wall in MonetDB. Commun. ACM 51(12), 77–85 (2008)
Anicic, D., Fodor, P., Rudolph, S., Stojanovic, N.: EP-SPARQL: a unified language for event processing and stream reasoning. In: Proceedings of WWW 2011, pp. 635–644 (2011)
Atre, A., Chaoji, V., Zaki, M.J., Hendler, J.A.: Matrix “Bit" loaded: a scalable lightweight join query processor for RDF data. In: Proceedings of WWW 2010, pp. 41–50 (2010)
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)
Barbieri, D.F., Braga, D., Ceri, S., Grossniklaus, M.: An execution environment for C-SPARQL queries. In: Proceedings of EDBT 2010, pp. 441–452 (2010)
Calbimonte, J.-P., Corcho, O., Gray, A.J.G.: Enabling ontology-based access to streaming data sources. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010. LNCS, vol. 6496, pp. 96–111. Springer, Heidelberg (2010). doi:10.1007/978-3-642-17746-0_7
Carroll, J.J., Dickinson, I., Dollin, C., Reynolds, D., Seaborne, A., Wilkinson, K.: Jena: implementing the semantic web recommendations. In: Proceedings of WWW 2004 (Alternate Track Papers & Posters), pp. 74–83 (2004)
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)
Hoeksema, J., Kotoulas, S.: High-performance distributed stream reasoning using s4. In: Proceedings of Ordring Workshop at ISWC 2011 (2011)
Khrouf, H., Belabbess, B., Bihanic, L., Kepeklian, G., Curé, O.: WAVES: big data platform for real-time RDF stream processing. In: Proceedings of SR+SWIT@ISWC 2016, pp. 37–48 (2016)
Kolchin, M., Wetz, P., Kiesling, E., Tjoa, A.M.: YABench: a comprehensive framework for RDF stream processor correctness and performance assessment. In: Bozzon, A., Cudre-Maroux, P., Pautasso, C. (eds.) ICWE 2016. LNCS, vol. 9671, pp. 280–298. Springer, Cham (2016). doi:10.1007/978-3-319-38791-8_16
Le-Phuoc, D., Dao-Tran, M., Xavier Parreira, J., Hauswirth, M.: A native and adaptive approach for unified processing of linked streams and linked data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 370–388. Springer, Heidelberg (2011). doi:10.1007/978-3-642-25073-6_24
Le-Phuoc, D., Nguyen Mau Quoc, H., Le Van, C., Hauswirth, M.: Elastic and scalable processing of linked stream data in the cloud. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 280–297. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41335-3_18
Li, Q., Zhang, X., Feng, Z.: PRSP: a plugin-based framework for RDF stream processing. In: Proceedings of WWW 2017, poster, pp. 815–816 (2017)
Margara, A., Cugola, G.: Processing flows of information: from data stream to complex event processing. In: Proceedings of DEBS 2011, pp. 359–360 (2011)
Margara, A., Urbani, J., Van Harmelen, F., Bal, H.: Streaming the web: reasoning over dynamic data. J. Web Semant. 25(1), 24–44 (2014)
Neumann, T., Weikum, G.: The RDF-3X engine for scalable management of RDF data. VLDB J. 19(1), 91–113 (2010)
Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 30–43. Springer, Heidelberg (2006). doi:10.1007/11926078_3
Peng, P., Zou, L., Özsu, M.T., Chen, L., Zhao, D.: Processing SPARQL queries over distributed RDF graphs. VLDB J. 25(2), 243–268 (2016)
Zou, L., Özsu, M.T., Chen, L., Shen, X., Huang, R., Zhao, D.: gStore: a graph-based SPARQL query engine. VLDB J. 23(4), 565–590 (2014)
Acknowledgments
This work is supported by the programs of the National Natural Science Foundation of China (61672377), the National Key Research and Development Program of China (2016YFB1000603), and the Key Technology Research and Development Program of Tianjin (16YFZCGX00210).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, Q., Zhang, X., Feng, Z. (2017). An Adaptive Framework for RDF Stream Processing. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-63579-8_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63578-1
Online ISBN: 978-3-319-63579-8
eBook Packages: Computer ScienceComputer Science (R0)