DRain: An Engine for Quality-of-Result Driven Process-Based Data Analytics

  • Aitor Murguzur
  • Johannes M. Schleicher
  • Hong-Linh Truong
  • Salvador Trujillo
  • Schahram Dustdar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8659)


The analysis of massive amounts of diverse data provided by large cities, combined with the requirements from multiple domain experts and users, is becoming a challenging trend. Although current process-based solutions rise in data awareness, there is less coverage of approaches dealing with the Quality-of-Result (QoR) to assist data analytics in distributed data-intensive environments. In this paper, we present the fundamental building blocks of a framework for enabling process selection and configuration through user-defined QoR at runtime. These building blocks form the basis to support modeling, execution and configuration of data-aware process variants in order to perform analytics. They can be integrated with different underlying APIs, promoting abstraction, QoR-driven data interaction and configuration. Finally, we carry out a preliminary evaluation on the URBEM scenario, concluding that our framework spends little time on QoR-driven selection and configuration of data-aware processes.


Data-aware Processes Runtime Configuration Data Analytics Smart Cities 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Aitor Murguzur
    • 1
  • Johannes M. Schleicher
    • 2
  • Hong-Linh Truong
    • 2
  • Salvador Trujillo
    • 1
  • Schahram Dustdar
    • 2
  1. 1.Software Production AreaIK4-Ikerlan Research CenterSpain
  2. 2.Distributed System GroupVienna University of TechnologyAustria

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