Linked Stream Data Processing Engines: Facts and Figures View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2012

AUTHORS

Danh Le-Phuoc , Minh Dao-Tran , Minh-Duc Pham , Peter Boncz , Thomas Eiter , Michael Fink

ABSTRACT

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. More... »

PAGES

300-312

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-35173-0_20

DOI

http://dx.doi.org/10.1007/978-3-642-35173-0_20

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1046964282


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