Advertisement

A Compressed, Inference-Enabled Encoding Scheme for RDF Stream Processing

  • Jérémy Lhez
  • Xiangnan Ren
  • Badre Belabbess
  • Olivier Curé
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10250)

Abstract

The number of sensors producing data streams at a high velocity keeps increasing. This paper describes an attempt to design an inference-enabled, distributed, fault-tolerant framework targeting RDF streams in the context of an industrial project. Our solution gives a special attention to the latency issue, an important feature in the context of providing reasoning services. Low latency is attained by compressing the scheme and data of processed streams with a dedicated semantic-aware encoding solution. After providing an overview of our architecture, we detail our encoding approach which supports a trade-off between two common inference methods, i.e., materialization and query reformulation. The analysis of results of our prototype emphasize the relevance of our design choices.

Notes

Acknowledgment

This work has been supported by the Waves project which is partially supported by the French FUI (Fonds Unique Interministériel) call #17.

References

  1. 1.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, Cambridge (2003)MATHGoogle Scholar
  2. 2.
    Barbieri, D.F., Braga, D., Ceri, S., Valle, E.D., Grossniklaus, M.: C-SPARQL: SPARQL for continuous querying. In: Proceedings of the 18th International Conference on World Wide Web, pp. 1061–1062 (2009)Google Scholar
  3. 3.
    Barbieri, D.F., Braga, D., Ceri, S., Valle, E., Grossniklaus, M.: Incremental reasoning on streams and rich background knowledge. In: Aroyo, L., Antoniou, G., Hyvönen, E., Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010. LNCS, vol. 6088, pp. 1–15. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-13486-9_1CrossRefGoogle Scholar
  4. 4.
    Calbimonte, J.-P., Mora, J., Corcho, O.: Query rewriting in RDF stream processing. In: Sack, H., Blomqvist, E., d’Aquin, M., Ghidini, C., Ponzetto, S.P., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9678, pp. 486–502. Springer, Cham (2016). doi: 10.1007/978-3-319-34129-3_30CrossRefGoogle Scholar
  5. 5.
    Curé, O., Naacke, H., Randriamalala, T., Amann, B.: LiteMat: a scalable, cost-efficient inference encoding scheme for large RDF graphs. In: 2015 IEEE International Conference on Big Data, Big Data 2015, pp. 1823–1830 (2015)Google Scholar
  6. 6.
    Fernández, J.D., Llaves, A., Corcho, O.: Efficient RDF interchange (ERI) format for RDF data streams. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8797, pp. 244–259. Springer, Cham (2014). doi: 10.1007/978-3-319-11915-1_16CrossRefGoogle Scholar
  7. 7.
    Fisteus, J.A., Garcia, N.F., Fernandez, L.S., Fuentes-Lorenzo, D.: Ztreamy: a middleware for publishing semantic streams on the web. Web Semant. Sci. Serv. Agents World Wide Web 25, 16–23 (2014)CrossRefGoogle Scholar
  8. 8.
    Fernández, N., Arias, J., Sánchez, L., Fuentes-Lorenzo, D., Corcho, Ó.: RDSZ: an approach for lossless RDF stream compression. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 52–67. Springer, Cham (2014). doi: 10.1007/978-3-319-07443-6_5CrossRefGoogle Scholar
  9. 9.
    Gupta, A., Mumick, I.S., Subrahmanian, V.S.: Maintaining views incrementally. SIGMOD Rec. 22(2), 157–166 (1993)CrossRefGoogle Scholar
  10. 10.
    Muñoz, S., Pérez, J., Gutierrez, C.: Simple and efficient minimal RDFS. J. Web Sem. 7(3), 220–234 (2009)CrossRefGoogle Scholar
  11. 11.
    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_18CrossRefGoogle Scholar
  12. 12.
    Toshniwal, A., Taneja, S., Shukla, A., Ramasamy, K., Patel, J.M., Kulkarni, S., Jackson, J., Gade, K., Fu, M., Donham, J., Bhagat, N., Mittal, S., Ryaboy, D.: Storm@twitter. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, SIGMOD 2014, pp. 147–156 (2014)Google Scholar
  13. 13.
    Urbani, J., Maassen, J., Bal, H.E.: Massive semantic web data compression with mapreduce. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, HPDC 2010, pp. 795–802 (2010)Google Scholar
  14. 14.
    Wang, G., Koshy, J., Subramanian, S., Paramasivam, K., Zadeh, M., Narkhede, N., Rao, J., Kreps, J., Stein, J.: Building a replicated logging system with apache Kafka. PVLDB 8(12), 1654–1665 (2015)Google Scholar
  15. 15.
    Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: 2nd USENIX Workshop on Hot Topics in Cloud Computing, HotCloud 2010 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jérémy Lhez
    • 1
  • Xiangnan Ren
    • 1
    • 2
    • 3
  • Badre Belabbess
    • 1
    • 2
  • Olivier Curé
    • 1
  1. 1.LIGM (UMR 8049), CNRS, ENPC, ESIEE, UPEMMarne-la-valléeFrance
  2. 2.AtosBezonsFrance
  3. 3.ISEP - LISITEParisFrance

Personalised recommendations