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|Title=EP-SPARQL: A Unified Language for Event Processing and Stream Reasoning
 
|Title=EP-SPARQL: A Unified Language for Event Processing and Stream Reasoning
 
|Year=2011
 
|Year=2011
|Booktitle=WWW 2011
+
|Booktitle=Proceedings of the 20th International Conference on World Wide Web, WWW 2011
 +
|Pages=635-644
 
|Publisher=ACM
 
|Publisher=ACM
|Note=accepted for publication
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|Editor=Sadagopan Srinivasan, Krithi Ramamritham, Arun Kumar, M. P. Ravindra, Elisa Bertino, Ravi Kumar
 
}}
 
}}
 
{{Publikation Details
 
{{Publikation Details
 
|Abstract=Streams of events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data etc. Event Processing (EP) is concerned with timely detection of compound events within streams of simple events. State-of-the-art EP provides on-the-fly analysis of event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by event streams. To bridge the gap, we propose Event Processing SPARQL (EP-SPARQL), as a new language for complex events and Stream Reasoning. We provide syntax and formal semantics of the language and devise an effective execution model for the proposed formalism. The execution model is grounded on logic rules, and features both effective event processing and inference capabilities over temporal and static knowledge. We provide an open-source prototype implementation, and present a set of tests to show the usefulness and effectiveness of our approach.
 
|Abstract=Streams of events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data etc. Event Processing (EP) is concerned with timely detection of compound events within streams of simple events. State-of-the-art EP provides on-the-fly analysis of event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by event streams. To bridge the gap, we propose Event Processing SPARQL (EP-SPARQL), as a new language for complex events and Stream Reasoning. We provide syntax and formal semantics of the language and devise an effective execution model for the proposed formalism. The execution model is grounded on logic rules, and features both effective event processing and inference capabilities over temporal and static knowledge. We provide an open-source prototype implementation, and present a set of tests to show the usefulness and effectiveness of our approach.
|Download=Www29-anicic.pdf,  
+
|Download=Www29-anicic.pdf,
 
|Projekt=ExpresST
 
|Projekt=ExpresST
 
|Forschungsgruppe=Wissensmanagement
 
|Forschungsgruppe=Wissensmanagement
 
}}
 
}}

Aktuelle Version vom 8. Juli 2011, 10:48 Uhr


EP-SPARQL: A Unified Language for Event Processing and Stream Reasoning


EP-SPARQL: A Unified Language for Event Processing and Stream Reasoning



Published: 2011
Herausgeber: Sadagopan Srinivasan, Krithi Ramamritham, Arun Kumar, M. P. Ravindra, Elisa Bertino, Ravi Kumar
Buchtitel: Proceedings of the 20th International Conference on World Wide Web, WWW 2011
Seiten: 635-644
Verlag: ACM

Referierte Veröffentlichung

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Kurzfassung
Streams of events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data etc. Event Processing (EP) is concerned with timely detection of compound events within streams of simple events. State-of-the-art EP provides on-the-fly analysis of event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by event streams. To bridge the gap, we propose Event Processing SPARQL (EP-SPARQL), as a new language for complex events and Stream Reasoning. We provide syntax and formal semantics of the language and devise an effective execution model for the proposed formalism. The execution model is grounded on logic rules, and features both effective event processing and inference capabilities over temporal and static knowledge. We provide an open-source prototype implementation, and present a set of tests to show the usefulness and effectiveness of our approach.

Download: Media:Www29-anicic.pdf

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