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Datenbank-Spektrum

, Volume 15, Issue 2, pp 119–129 | Cite as

Complex Event Processing on Linked Stream Data

  • Omran Saleh
  • Stefan Hagedorn
  • Kai-Uwe Sattler
SCHWERPUNKTBEITRAG

Abstract

Social networks and Sensor Web technologies typically generate a massive amount of data published as streams. In order to give these streams a meaningful sense and enrich them with semantic descriptions, the concept of Linked Stream Data (LSD) has emerged. However, to support a wide range of LSD scenarios and queries comprehensive solutions providing not only classic data stream operators such as windows, but also for processing of complex events, linking of (static) datasets, and scalable processing are required. In this paper, we present our approach for processing LSD and addressing these requirements. In contrast to existing LSD engines relying on streaming extensions to SPARQL, our PipeFlow system is a (relational) dataflow language and engine providing support for complex event processing (CEP) and a few dedicated operators for RDF data. We describe this language and particularly the CEP model as well as the system architecture for parallel CEP and LSD processing by exploiting partitioning techniques for cluster environments. Finally, we report results from experiments evaluating our system in comparison to existing LSD engines.

Keywords

Stream Processing Continuous Query Complex Event Processing Kleene Star Pattern Match Query 
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

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Computer Science and Automation, Database and Information Systems Group, Technische Universität IlmenauIlmenauGermany

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