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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Eviction strategies for semantic flow processing
Organization Unit
  • Minh Khoa Nguyen
  • Thomas Scharrenbach
  • Abraham Bernstein
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
ISSN 1613-0073
Page Range 66 - 80
Event Title SSWS 2013 - Scalable Semantic Web Knowledge Base Systems 2013
Event Type workshop
Event Location Sydney, Australia
Event Start Date October 21 - 2013
Event End Date October 21 - 2013
Series Name CEUR Workshop Proceedings
Number 1046
Place of Publication Aachen, Germany
Publisher CEUR-WS
Abstract Text In order to cope with the ever-increasing data volume continuous processing of incoming data via Semantic Flow Processing systems have been proposed. These systems allow to answer queries on streams of RDF triples. To achieve this goal they match (triple) patterns against the incoming stream and generate/update variable bindings. Yet, given the continuous nature of the stream the number of bindings can explode and exceed memory; in particular when computing aggregates. To make the information processing practical Semantic Flow Processing systems, therefore, typically limit the considered data to a (moving) window. Whilst this technique is simple it may not be able to nd patterns spread further than the window or may still cause memory overruns when data is highly bursty. In this paper we propose to maintain bindings (and thus memory) not on recency (i.e., a window) but on the likelihood of contributing to a complete match. We propose to base the decision on the matching likelihood and not creation time (fo) or at random. Furthermore we propose to drop variable bindings instead of data as do load shedding approaches. Specically, we systematically investigate deterministic and the matching-likelihood based probabilistic eviction strategy for dropping variable bindings in terms of recall. We find that a matching likelihood based eviction can outperform fo and random eviction strategies on synthetic as well as real world data.
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