Complex Event Processing on Linked Stream Data

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.