Reasoning on Data Streams: An Approach to Adaptation in Pervasive Systems

  • Nicola Bicocchi
  • Emil Vassev
  • Franco Zambonelli
  • Mike Hinchey
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 144)


Urban environments are increasingly invaded by devices that acquire sensor information and pave the way for innovative forms of context awareness. Collecting knowledge from loosely-structured data streams and reasoning about changes are two key elements of the process. This paper illustrates a possible way to combine these two elements in a coordinated way. We make use of a recently-developed framework for classifying data streams with service-oriented, reconfigurable components. Furthermore, we embed the KnowLang Reasoner, allowing logical and statistical reasoning on the acquired knowledge aiming to achieve self-adaptation.


Bayesian Network Data Stream Situational Awareness Smart City Boolean Expression 
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.


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Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015

Authors and Affiliations

  • Nicola Bicocchi
    • 1
  • Emil Vassev
    • 2
  • Franco Zambonelli
    • 1
  • Mike Hinchey
    • 2
  1. 1.Universita di Modena e Reggio EmiliaModenaItalia
  2. 2.Lero–the Irish Software Engineering Research CentreUniversity of LimerickLimerickIreland

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