A Data Services Composition Approach for Continuous Query on Social Media Streams

  • Guiling WangEmail author
  • Xiaojiang Zuo
  • Marc Hesenius
  • Yao Xu
  • Yanbo Han
  • Volker Gruhn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11360)


We witness a rapid increase in the number of social media streams due to development of Web2.0, IoT and Cloud Computing technology. These sources include both traditional relational databases and streaming data from messaging infrastructure. We would like to use multiple social media streams to answer complex queries to enable information sharing and intelligence gathering for better collaboration. For this purpose, we adopt data services as the basic abstraction for both traditional relational databases and data streams retrieval. A flexible continuous data service model with continuous query as service operation is proposed. Service operation instance is modeled as a view defined on data streams. In the view, data part and time synchronization part are separated from each other. Based on the continuous data service model, we proposed a continuous data service composition algorithm for answering queries across data streams and relational data. The main idea is to find the contained rewriting of user query on views satisfying both data part and time synchronization part containment relationship. We also present use case and experimental studies that indicate 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 (Building Stream Data Services for Spatio-Temporal Pattern Discovery in Cloud Computing Environment) and National Natural Science Foundation of China No. 61672042 (Models and Methodology of Data Services Facilitating Dynamic Correlation of Big Stream Data), and University Cooperation Projects Foundation of CETC Ocean Corp.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

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 CompanyChina Electronics Technology Group Corporation (CETC Ocean Corp.)BeijingChina
  3. 3.paluno - The Ruhr Institute for Software TechnologyUniversity of Duisburg-EssenEssenGermany

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