Complex Event Processing on Linked Stream Data

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 sce...

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Bibliographic Details
Published in:Datenbank-Spektrum : Zeitschrift für Datenbanktechnologie : Organ der Fachgruppe Datenbanken der Gesellschaft für Informatik e.V Vol. 15; no. 2; pp. 119 - 129
Main Authors: Saleh, Omran, Hagedorn, Stefan, Sattler, Kai-Uwe
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2015
Subjects:
Computer Science
Computer Systems Organization and Communication Networks
Data Mining and Knowledge Discovery
Data Structures and Information Theory
Database Management
Information Storage and Retrieval
IT in Business
Schwerpunktbeitrag
Continuous Query
Kleene Star
Complex Event Processing
Pattern Match Query
Stream Processing
ISSN:1618-2162, 1610-1995
Online Access:Get full text
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Summary: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.
ISSN:1618-2162
1610-1995
DOI:10.1007/s13222-015-0190-5