Approximate Continuous Query Answering over Streams and Dynamic Linked Data Sets

  • Soheila DehghanzadehEmail author
  • Daniele Dell’Aglio
  • Shen Gao
  • Emanuele Della Valle
  • Alessandra  Mileo
  • Abraham Bernstein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9114)


To perform complex tasks, RDF Stream Processing Web applications evaluate continuous queries over streams and quasi-static (background) data. While the former are pushed in the application, the latter are continuously retrieved from the sources. As soon as the background data increase the volume and become distributed over the Web, the cost to retrieve them increases and applications become unresponsive. In this paper, we address the problem of optimizing the evaluation of these queries by leveraging local views on background data. Local views enhance performance, but require maintenance processes, because changes in the background data sources are not automatically reflected in the application. We propose a two-step query-driven maintenance process to maintain the local view: it exploits information from the query (e.g., the sliding window definition and the current window content) to maintain the local view based on user-defined Quality of Service constraints. Experimental evaluation show the effectiveness of the approach.


Background Data Remote Service Maintenance Policy Maintenance Process Continuous Query 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aranda, C., Arenas, M., Corcho, Ó., Polleres, A.: Federating queries in SPARQL 1.1: Syntax, semantics and evaluation. J. Web Sem. 18(1), 1–17 (2013)CrossRefGoogle Scholar
  2. 2.
    Buil-Aranda, C., Polleres, A., Umbrich, J.: Strategies for executing federated queries in SPARQL1.1. In: Mika, P., et al. (eds.) ISWC 2014, Part II. LNCS, vol. 8797, pp. 390–405. Springer, Heidelberg (2014)Google Scholar
  3. 3.
    Babu, S., Munagala, K., Widom, J., Motwani, R.: Adaptive caching for continuous queries. In: ICDE 2005, pp. 118–129. IEEE (2005)Google Scholar
  4. 4.
    Barbieri, D.F., Braga, D., Ceri, S., Della Valle, E., Grossniklaus, M.: Querying RDF streams with C-SPARQL. SIGMOD Record 39(1), 20–26 (2010)CrossRefGoogle Scholar
  5. 5.
    Blanas, S., Patel, J.M., Ercegovac, V., Rao, J., Shekita, E.J., Tian, Y.: A comparison of join algorithms for log processing in mapreduce. In: SIGMOD 2010, pp. 975–986. ACM (2010)Google Scholar
  6. 6.
    Calbimonte, J., Jeung, H., Corcho, Ó., Aberer, K.: Enabling query technologies for the semantic sensor web. Int. J. Sem. Web Inf. Syst. 8(1), 43–63 (2012)CrossRefGoogle Scholar
  7. 7.
    Celino, I., Dell’Aglio, D., Della Valle, E., Huang, Y., Lee, T., Kim, S.-H., Tresp, V.: Towards BOTTARI: using stream reasoning to make sense of location-based micro-posts. In: García-Castro, R., Fensel, D., Antoniou, G. (eds.) ESWC 2011. LNCS, vol. 7117, pp. 80–87. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  8. 8.
    Goasdoué, F., Karanasos, K., Leblay, J., Manolescu, I.: View selection in semantic web databases. PVLDB 5(2), 97–108 (2011)Google Scholar
  9. 9.
    Guo, H., Larson, P., Ramakrishnan, R.: Caching with good enough currency, consistency, and completeness. In: VLDB, pp. 457–468. VLDB Endowment (2005)Google Scholar
  10. 10.
    Guo, H., Larson, P., Ramakrishnan, R., Goldstein, J.: Relaxed currency and consistency: how to say good enough in SQL. In: SIGMOD, pp. 815–826. ACM (2004)Google Scholar
  11. 11.
    Labrinidis, A., Roussopoulos, N.: Exploring the tradeoff between performance and data freshness in database-driven web servers. PVLDB 13(3), 240–255 (2004)Google Scholar
  12. 12.
    Le-Phuoc, D., Dao-Tran, M., Parreira, J.X., 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
  13. 13.
    Lécué, F., Tallevi-Diotallevi, S., Hayes, J., Tucker, R., Bicer, V., Sbodio, M.L., Tommasi, P.: Smart traffic analytics in the semantic web with STAR-CITY: Scenarios, system and lessons learned in Dublin City. J. Web Sem. 27, 26–33 (2014)CrossRefGoogle Scholar
  14. 14.
    Natsev, A., Chang, Y.-C., Smith, J.R., Li, C.-S., Vitter, J.S.: Supporting incremental join queries on ranked inputs. In: VLDB, pp. 281–290. Morgan Kaufmann (2001)Google Scholar
  15. 15.
    Parssian, A., Sarkar, S., Jacob, V.S.: Assessing information quality for the composite relational operation join. In: ICIQ, pp. 225–237. MIT (2002)Google Scholar
  16. 16.
    Schmidt, M., Meier, M., Lausen, G.: Foundations of sparql query optimization. In: ICDT, pp. 4–33. ACM (2010)Google Scholar
  17. 17.
    Sean, X., Xiaoquan, Z.: Impact of wikipedia on market information environment: Evidence on management disclosure and investor reaction. MIS Quarterly. Management Information Systems Research Center (2013)Google Scholar
  18. 18.
    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
  19. 19.
    Viglas, S.D., Naughton, J.F., Burger, J.: Maximizing the output rate of multi-way join queries over streaming information sources. In: VLDB, pp. 285–296. VLDB Endowment (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Soheila Dehghanzadeh
    • 1
    Email author
  • Daniele Dell’Aglio
    • 2
  • Shen Gao
    • 3
  • Emanuele Della Valle
    • 2
  • Alessandra  Mileo
    • 1
  • Abraham Bernstein
    • 3
  1. 1.INSIGHT Research CenterNUI GalwayGalwayIreland
  2. 2.DEIBPolitecnico of MilanoMilanItaly
  3. 3.Department of InformaticsUniversity of ZurichZurichSwitzerland

Personalised recommendations