Abstract

As data proliferates at increasing rates, the need for real-time stream processing applications increases as well. In the same way that Data Stream Management Systems (DSMS) have emerged from the database community, there is now a similar concern in managing dynamic knowledge among the Semantic Web community. Unfortunately, early relevant approaches are, to a large extent, theoretical and do not present convincing evidence of their efficiency in real dynamic environments. In this paper, we present a framework for the effective, real-time processing of streaming data and we define and analyse in depth its key components. Our framework serves as a basis for the implementation of the SensorStream prototype, on which we run numerous performance and scalability measurements that outline its behaviour and demonstrate its suitability and scalability for solutions that require real-time information processing from distributed and heterogeneous data sources.

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