A Data Services Composition Approach for Continuous Query on Data Streams

  • Guiling WangEmail author
  • Xiaojiang Zuo
  • Marc Hesenius
  • Yao Xu
  • Yanbo Han
  • Volker Gruhn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10988)


We witness a rapid increase in the number of data streams due to Cloud Computing, Big Data and IoT development. We would like to access and share data streams using a data service approach. In this paper, we propose a flexible continuous data service model and a continuous data service composition algorithm for answering queries across data streams. Service operation instance is modeled as a view defined on data streams composed of two parts: a data part and a time synchronization part. The composition algorithm extends the traditional Bucket algorithm to find the contained rewriting of user query on views satisfying the containment relationship of both data part and time synchronization part. We also present use case and experimental studies indicating that the approach is effective and efficient.


Data streams Query rewriting Data services Service composition Continuous query 



This work is supported by Beijing Natural Science Foundation No. 4172018, National Natural Science Foundation of China No. 61672042, and University Cooperation Projects Foundation of CETC Ocean Corp.


  1. 1.
    Carey, M.J., Onose, N., Petropoulos, M.: Data services. Commun. ACM 55(6), 86–97 (2012)CrossRefGoogle Scholar
  2. 2.
    Vaculín, R., Chen, H., Neruda, R., Sycara, K.: Modeling and discovery of data providing services. In: 2008 IEEE International Conference on Web Services, pp. 54–61, September 2008Google Scholar
  3. 3.
    Barhamgi, M., Benslimane, D., Ouksel, A.M.: Composing and optimizing data providing web services. In: Proceedings of the 17th International Conference on World Wide Web, pp. 1141–1142. ACM (2008)Google Scholar
  4. 4.
    Barhamgi, M., Benslimane, D., Medjahed, B.: A query rewriting approach for web service composition. IEEE Trans. Serv. Comput. 3(3), 206–222 (2010)CrossRefGoogle Scholar
  5. 5.
    Zhou, L., Chen, H., Yu, T., Ma, J., Wu, Z.: Ontology-based scientific data service composition: a query rewriting-based approach. In: AAAI Spring Symposium: Semantic Scientific Knowledge Integration, pp. 116–121 (2008)Google Scholar
  6. 6.
    Zhang, F., Wang, G., Han, Y.: Automatic generation of service composition plans for correlated queries. In: 2013 10th Web Information System and Application Conference, pp. 143–149, November 2013Google Scholar
  7. 7.
    Ghanem, T.M., Elmagarmid, A.K., Larson, P.Å., Aref, W.G.: Supporting views in data stream management systems. ACM Trans. Database Syst. 35(1), 1–47CrossRefGoogle Scholar
  8. 8.
    Levy, A.Y., Rajaraman, A., Ordille, J.J.: The world wide web as a collection of views: query processing in the information manifold. In: VIEWS, pp. 43–55 (1996)Google Scholar
  9. 9.
    Doan, A., Halevy, A., Ives, Z.: Principles of Data Integration, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco (2012)Google Scholar
  10. 10.
    Pottinger, R., Halevy, A.: MiniCon: a scalable algorithm for answering queries using views. Int. J. Very Large Data Bases 10(2–3), 182–198 (2001)zbMATHGoogle Scholar
  11. 11.
    Hickson, I.: Server-sent events. Accessed 25 October 2015
  12. 12.
    Zhao, W., Liu, C., Chen, J.: Automatic composition of information-providing web services based on query rewriting. Sci. China Inf. Sci. 55(11), 2428–2444 (2012)CrossRefGoogle Scholar
  13. 13.
    Wang, G., Yang, S., Han, Y.: Mashroom: end-user mashup programming using nested tables. In: Proceedings of the 18th International Conference on World Wide Web, pp. 861–870. ACM (2009)Google Scholar
  14. 14.
    Han, Y., Wang, G., Ji, G., Zhang, P.: Situational data integration with data services and nested table. Serv. Oriented Comput. Appl. 7(2), 129–150 (2013)CrossRefGoogle Scholar
  15. 15.
    Genesereth, M.R., Keller, A.M., Duschka, O.M.: Infomaster: an information integration system. SIGMOD Rec. 26(2), 539–542 (1997)CrossRefGoogle Scholar
  16. 16.
    Levy, A.Y., Rajaraman, A., Ordille, J.J.: Querying heterogeneous information sources using source descriptions. In: Proceedings of the 22th International Conference on Very Large Data Bases. In: VLDB 1996, pp. 251–262. Morgan Kaufmann Publishers Inc., San Francisco (1996)Google Scholar
  17. 17.
    Han, Y., Liu, C., Su, S., Zhu, M., Zhang, Z., Zhang, S.: A proactive service model facilitating stream data fusion and correlation. Int. J. Web Serv. Res. (IJWSR) 14(3), 1–16 (2017)CrossRefGoogle Scholar
  18. 18.
    Gil, D., Ferrández, A., Mora-Mora, H., Peral, J.: Internet of things: a review of surveys based on context aware intelligent services. Sensors 16(7), 1069 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Guiling Wang
    • 1
    • 2
    Email author
  • Xiaojiang Zuo
    • 1
  • Marc Hesenius
    • 3
  • Yao Xu
    • 2
  • Yanbo Han
    • 1
  • Volker Gruhn
    • 3
  1. 1.Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream DataNorth China University of TechnologyBeijingChina
  2. 2.Ocean Information Technology Company, China Electronics Technology Group Corporation (CETC Ocean Corp.)BeijingChina
  3. 3.paluno - The Ruhr Institute for Software Technology, University of Duisburg-EssenEssenGermany

Personalised recommendations