TripleWave: Spreading RDF Streams on the Web

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9982)


Processing data streams is increasingly gaining momentum, given the need to process these flows of information in real-time and at Web scale. In this context, RDF Stream Processing (RSP) and Stream Reasoning (SR) have emerged as solutions to combine semantic technologies with stream and event processing techniques. Research in these areas has proposed an ecosystem of solutions to query, reason and perform real-time processing over heterogeneous and distributed data streams on the Web. However, so far one basic building block has been missing: a mechanism to disseminate and exchange RDF streams on the Web. In this work we close this gap, proposing TripleWave, a reusable and generic tool that enables the publication of RDF streams on the Web. The features of TripleWave were selected based on requirements of real use-cases, and support a diverse set of scenarios, independent of any specific RSP implementation. TripleWave can be fed with existing Web streams (e.g. Twitter and Wikipedia streams) or time-annotated RDF datasets (e.g. the Linked Sensor Data dataset). It can be invoked through both pull- and push-based mechanisms, thus enabling RSP engines to automatically register and receive data from TripleWave.


Streaming Data Stream Element Stream Graph Link Data Principle Stream Processing Engine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Anicic, D., Fodor, P., Rudolph, S., Stojanovic, N.: EP-SPARQL: a unified language for event processing and stream reasoning. In: WWW, pp. 635–644. ACM (2011)Google Scholar
  2. 2.
    Balduini, M., Della Valle, E., Dell’Aglio, D., Tsytsarau, M., Palpanas, T., Confalonieri, C.: Social listening of city scale events using the streaming linked data framework. In: Alani, H., et al. (eds.) The Semantic Web – ISWC 2013. LNCS, vol. 8219, pp. 1–16. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Barbieri, D.F., Braga, D., Ceri, S., Della Valle, E., Grossniklaus, M.: C-sparql: a continuous query language for rdf data streams. Intl. J. Semant. Comput. 4(01), 3–25 (2010)CrossRefzbMATHGoogle Scholar
  4. 4.
    Barbieri, D.F., Della Valle, E.: A proposal for publishing data streams as linked data - A position paper. In: LDOW (2010)Google Scholar
  5. 5.
    Berners-Lee, T., Bizer, C., Heath, T.: Linked data-the story so far. IJSWIS 5(3), 1–22 (2009)Google Scholar
  6. 6.
    Calbimonte, J.-P., Jeung, H., Corcho, O., Aberer, K.: Enabling query technologies for the semantic sensor web. Int. J. Semant. Web Inf. Syst. 8, 43–63 (2012)CrossRefGoogle 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. J. Web Semant. 25, 16–23 (2014)CrossRefGoogle Scholar
  8. 8.
    Gerber, D., Hellmann, S., Bühmann, L., Soru, T., Usbeck, R., Ngonga Ngomo, A.-C.: Real-Time RDF extraction from unstructured data streams. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 135–150. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-41335-3_9 CrossRefGoogle Scholar
  9. 9.
    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 CrossRefGoogle Scholar
  10. 10.
    Le-Phuoc, D., Nguyen-Mau, H.Q., Parreira, J.X., Hauswirth, M.: A middleware framework for scalable management of linked streams. J. Web Semant. 16, 42–51 (2012)CrossRefGoogle Scholar
  11. 11.
    Le-Phuoc, D., Quoc, H.N.M., Quoc, H.N., Nhat, T.T., Hauswirth, M.: The graph of things: A step towards the live knowledge graph of connected things. J. Web Semant. 37, 25–35 (2016)CrossRefGoogle Scholar
  12. 12.
    Lécué, F., Tallevi-Diotallevi, S., Hayes, J., Tucker, R., Bicer, V., Sbodio, M.L., Tommasi, P.: Star-city: semantic traffic analytics and reasoning for city. In: ACM IUI, pp. 179–188 (2014)Google Scholar
  13. 13.
    Mauri, A., Calbimonte, J.-P., Dell’Aglio, D., Balduini, M., Della Valle, E., Aberer, K.: Where are the rdf streams?: Deploying rdf streams on the web of data with triplewave. In: Poster Proceedings of ISWC (2015)Google Scholar
  14. 14.
    Patni, H., Henson, C., Sheth, A.: Linked sensor data. In: IEEE CTS, pp. 362–370 (2010)Google Scholar
  15. 15.
    Scharrenbach, T., Urbani, J., Margara, A., Valle, E., Bernstein, A.: Seven commandments for benchmarking semantic flow processing systems. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 305–319. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38288-8_21 CrossRefGoogle Scholar
  16. 16.
    Trinh, T.-D., Wetz, P., Do, B.-L., Anjomshoaa, A., Kiesling, E., Tjoa, A.M.: A web-based platform for dynamic integration of heterogeneous data. In: IIWAS, pp. 253–261 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.DEIB, Politecnico di MilanoMilanItaly
  2. 2.EPFLLausanneSwitzerland
  3. 3.HES-SO ValaisSierreSwitzerland

Personalised recommendations