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Incremental Rule-based Reasoning on Semantic Data Streams

Incremental Rule-based Reasoning on Semantic Data Streams
Incremental Rule-based Reasoning on Semantic Data Streams
This thesis investigates the area of semantic stream processing, in which data streams are combined with semantic reasoning techniques. We have investigated techniques for rule-based reasoning over semantic streams in which reasoning is implemented natively over streams as data flow networks, and have developed an adaptive optimisation method to cope with the changing nature of streams. The contributions of this thesis include R4, a native rule-based reasoner for RDF streams using the Rete algorithm, and a cost-based adaptive plan optimiser designed for RDF streams. We have evaluated the performance of R4 and compared it to both a typical static reasoner and to the state-of-the-art in stream reasoners. The results show that R4 significantly outperforms these reasoners in terms of throughput. We have also evaluated the adaptive optimisation technique, with results that show the ability of the optimiser to devise and adopt better performing plans at runtime.
University of Southampton
Albeladi, Rehab
285fc259-3c7c-43ea-88ca-250f27188327
Albeladi, Rehab
285fc259-3c7c-43ea-88ca-250f27188327
Gibbins, Nicholas
98efd447-4aa7-411c-86d1-955a612eceac

Albeladi, Rehab (2016) Incremental Rule-based Reasoning on Semantic Data Streams. University of Southampton, Doctoral Thesis, 227pp.

Record type: Thesis (Doctoral)

Abstract

This thesis investigates the area of semantic stream processing, in which data streams are combined with semantic reasoning techniques. We have investigated techniques for rule-based reasoning over semantic streams in which reasoning is implemented natively over streams as data flow networks, and have developed an adaptive optimisation method to cope with the changing nature of streams. The contributions of this thesis include R4, a native rule-based reasoner for RDF streams using the Rete algorithm, and a cost-based adaptive plan optimiser designed for RDF streams. We have evaluated the performance of R4 and compared it to both a typical static reasoner and to the state-of-the-art in stream reasoners. The results show that R4 significantly outperforms these reasoners in terms of throughput. We have also evaluated the adaptive optimisation technique, with results that show the ability of the optimiser to devise and adopt better performing plans at runtime.

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Published date: July 2016

Identifiers

Local EPrints ID: 419658
URI: https://eprints.soton.ac.uk/id/eprint/419658
PURE UUID: dfd5d4a3-b130-49b8-a1b8-17a3c6f4a79b
ORCID for Nicholas Gibbins: ORCID iD orcid.org/0000-0002-6140-9956

Catalogue record

Date deposited: 18 Apr 2018 16:32
Last modified: 14 Mar 2019 05:11

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Contributors

Author: Rehab Albeladi
Thesis advisor: Nicholas Gibbins ORCID iD

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