Applying Semantic Interoperability Principles to Data Stream Management

  • Daniele Dell’Aglio
  • Marco Balduini
  • Emanuele Della Valle
Part of the Data-Centric Systems and Applications book series (DCSA)

Abstract

More and more applications require real-time fine-grained query answering on massive, heterogeneous, noisy and incomplete data streams. Indeed, systems capable of scalable stream processing exist. Specialised data stream management systems (DSMSs) and complex event processing (CEP) have been largely investigated in the 2000s. They can provide reactive fine-grained information access even in the presence of noisy data. What they lack is the ability to master heterogeneity and incompleteness. In this chapter, we show out to apply semantic interoperability principles to data streams. In particular, we described recently developed methods that use extensions of semantic Web technologies (i.e. RDF, SPARQL and OWL) to continuously answer fine-grained query on heterogeneous and incomplete data streams in a scalable manner. To make the chapter easier to follow, examples are provided in the context of sensor network and social media analytics.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Daniele Dell’Aglio
    • 1
  • Marco Balduini
    • 1
  • Emanuele Della Valle
    • 1
  1. 1.Dipartimento di ElettronicaInformazione e Bioingegneria, Politecnico of MilanoMilanoItaly

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