When a FILTER Makes the Difference in Continuously Answering SPARQL Queries on Streaming and Quasi-Static Linked Data

  • Shima Zahmatkesh
  • Emanuele Della Valle
  • Daniele Dell’Aglio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9671)


We are witnessing a growing interest for Web applications that (i) require to continuously combine highly dynamic data stream with background data and (ii) have reactivity as key performance indicator. The Semantic Web community showed that RDF Stream Processing (RSP) is an adequate framework to develop this type of applications.

However, when the background data is distributed over the Web, even RSP engines risk losing reactiveness due to the time necessary to access the background data. State-of-the-art RSP engines remain reactive using a local replica of the background data, but such a replica progressively become stale if not updated to reflect the changes in the remote background data.

For this reason, recently, the RSP community investigated maintenance policies (collectively named Acqua) that guarantee reactiveness while maximizing the freshness of the replica. Acqua’s policies apply to queries that join a basic graph pattern in a window clause with another basic graph pattern in a service clause. In this paper, we extend the class of queries considered in Acqua adding a FILTER clause that selects mapping in the background data. We propose a new maintenance policy (namely, the Filter Update Policy) and we show how to combine it with Acqua policies. A set of experimental evaluations empirically proves the ability of the proposed policies to guarantee reactiveness while keeping the replica fresher than with the Acqua policies.


Background Data Graph Pattern Jaccard Index Query Evaluation Maintenance Policy 
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.



I would like to acknowledge the support of Soheila Dehghanzadeh and to thank her for the kind help in understanding the code base and the data set of Acqua.


  1. 1.
    Aranda, C.B., Arenas, M., Corcho, Ó., Polleres, A.: Federating queries in SPARQL 1.1: syntax, semantics and evaluation. J. Web Seman. 18(1), 1–17 (2013)CrossRefGoogle Scholar
  2. 2.
    Babu, S., Munagala, K., Widom, J., Motwani, R.: Adaptive caching for continuous queries. In: Proceedings of the 21st International Conference on Data Engineering, ICDE 2005, pp. 118–129. IEEE (2005)Google Scholar
  3. 3.
    Barbieri, D.F., Braga, D., Ceri, S., Della Valle, E., Grossniklaus, M.: Querying RDF streams with C-SPARQL. ACM SIGMOD Rec. 39(1), 20–26 (2010)CrossRefzbMATHGoogle Scholar
  4. 4.
    Calbimonte, J.-P., Jeung, H.Y., Corcho, O., Aberer, K.: Enabling query technologies for the semantic sensor web. Int. J. Seman. Web Inf. Syst. 8, 43–63 (2012)CrossRefGoogle Scholar
  5. 5.
    Dehghanzadeh, S., Dell’Aglio, D., Gao, S., Della Valle, E., Mileo, A., Bernstein, A.: Approximate continuous query answering over streams and dynamic linked data sets. In: Cimiano, P., Frasincar, F., Houben, G.-J., Schwabe, D. (eds.) ICWE 2015. LNCS, vol. 9114, pp. 307–325. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  6. 6.
    Dell’Aglio, D., Della Valle, E., Calbimonte, J., Corcho, Ó.: RSP-QL semantics: a unifying query model to explain heterogeneity of RDF stream processing systems. Int. J. Seman. Web Inf. Syst. 10(4), 17–44 (2014)CrossRefGoogle Scholar
  7. 7.
    Guo, H., Larson, P.-Å., Ramakrishnan, R.: Caching with good enough currency, consistency, and completeness. In: Proceedings of the 31st International Conference on Very Large Data Bases, pp. 457–468. VLDB Endowment (2005)Google Scholar
  8. 8.
    Ilyas, I.F., Beskales, G., Soliman, M.A.: A survey of top-k query processing techniques in relational database systems. ACM Comput. Surv. 40(4) (2008)Google Scholar
  9. 9.
    Labrinidis, A., Roussopoulos, N.: Exploring the tradeoff between performance and data freshness in database-driven web servers. VLDB J. 13(3), 240–255 (2004)CrossRefGoogle Scholar
  10. 10.
    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, Part I. LNCS, vol. 7031, pp. 370–388. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Magliacane, S., Bozzon, A., Della Valle, E.: Efficient execution of top-K SPARQL queries. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 344–360. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. ACM Trans. Database Syst. 34(3) (2009)Google Scholar
  13. 13.
    Umbrich, J., Karnstedt, M., Hogan, A., Parreira, J.X.: Freshening up while staying fast: towards hybrid SPARQL queries. In: ten Teije, A., Völker, J., Handschuh, S., Stuckenschmidt, H., d’Acquin, M., Nikolov, A., Aussenac-Gilles, N., Hernandez, N. (eds.) EKAW 2012. LNCS, vol. 7603, pp. 164–174. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Viglas, S.D., Naughton, J.F., Burger, J.: Maximizing the output rate of multi-way join queries over streaming information sources. In: Proceedings of the 29th International Conference on Very Large Data Bases, vol. 29, pp. 285–296. VLDB Endowment (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Shima Zahmatkesh
    • 1
  • Emanuele Della Valle
    • 1
  • Daniele Dell’Aglio
    • 1
  1. 1.Department of Electronics, Information and BioengineeringPolitecnico of MilanoMilanItaly

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