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Running out of Bindings? Integrating Facts and Events in Linked Data Stream Processing


Gao, Shen; Scharrenbach, Thomas; Kietz, Jörg-Uwe; Bernstein, Abraham (2015). Running out of Bindings? Integrating Facts and Events in Linked Data Stream Processing. In: 4th International Workshop on Ordering and Reasoning, Bethlehem, PA, USA, 11 October 2015 - 12 October 2015.

Abstract

Processing streams of linked data has gained increased importance over the past years. In many cases the streams contain events generated by sensors such as traffic control systems or news releases. As a reaction to this increased need, a number of languages and systems were developed that are aimed at processing linked data streams. These systems/languages follow one of two pertinent traditions: either they perform complex event processing or stream reasoning. However, both kinds of systems only support simulating system states as a sequence of events.
This paper proposes to model a new kind of data – Facts. Facts are temporal states stored in systems that combine events. Essentially, they trade space complexity for time complexity and reduce the intermediate variable bindings compared to other approaches. They also have the advantage of keeping queries relatively simple. In our evaluation, we compile queries for typical sensor-based use-cases in TEF-SPARQL, our SPARQL extension supporting Facts, C-SPARQL, and EP-SPARQL to the well-established Event Processing Language (EPL) running on the Esper complex event processing engine. Compared to simulate Facts, we show that modeling Facts directly only creates less than 1% of intermediate bindings and improves the throughput by up to 4 times.

Abstract

Processing streams of linked data has gained increased importance over the past years. In many cases the streams contain events generated by sensors such as traffic control systems or news releases. As a reaction to this increased need, a number of languages and systems were developed that are aimed at processing linked data streams. These systems/languages follow one of two pertinent traditions: either they perform complex event processing or stream reasoning. However, both kinds of systems only support simulating system states as a sequence of events.
This paper proposes to model a new kind of data – Facts. Facts are temporal states stored in systems that combine events. Essentially, they trade space complexity for time complexity and reduce the intermediate variable bindings compared to other approaches. They also have the advantage of keeping queries relatively simple. In our evaluation, we compile queries for typical sensor-based use-cases in TEF-SPARQL, our SPARQL extension supporting Facts, C-SPARQL, and EP-SPARQL to the well-established Event Processing Language (EPL) running on the Esper complex event processing engine. Compared to simulate Facts, we show that modeling Facts directly only creates less than 1% of intermediate bindings and improves the throughput by up to 4 times.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:12 October 2015
Deposited On:02 Sep 2015 14:23
Last Modified:30 May 2016 20:40
Publisher:s.n.
Other Identification Number:merlin-id:12307

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