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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Linked Stream Data Processing Engines: Facts and Figures
verfasst von : Danh Le-Phuoc, Minh Dao-Tran, Minh-Duc Pham, Peter Boncz, Thomas Eiter, Michael Fink
Erschienen in: The Semantic Web – ISWC 2012
Verlag: Springer Berlin Heidelberg
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Linked Stream Data, i.e., the RDF data model extended for representing stream data generated from sensors social network applications, is gaining popularity. This has motivated considerable work on developing corresponding data models associated with processing engines. However, current implemented engines have not been thoroughly evaluated to assess their capabilities. For reasonable systematic evaluations, in this work we propose a novel, customizable evaluation framework and a corresponding methodology for realistic data generation, system testing, and result analysis. Based on this evaluation environment, extensive experiments have been conducted in order to compare the state-of-the-art LSD engines wrt. qualitative and quantitative properties, taking into account the underlying principles of stream processing. Consequently, we provide a detailed analysis of the experimental outcomes that reveal useful findings for improving current and future engines.