Enhancing the scalability of expressive stream reasoning via input-driven parallelization-Reference-Cited by-同舟云学术

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Enhancing the scalability of expressive stream reasoning via input-driven parallelization

Published:2019-04-05 Issue:3 Volume:10 Page:457-474
ISSN:2210-4968
Container-title:Semantic Web
language:
Short-container-title:SW

Author:

Pham Thu-Le¹,Ali Muhammad Intizar¹,Mileo Alessandra²

Affiliation:

1. Insight Centre for Data Analytics, National University of Ireland, Galway, IDA Bussiness Park, Lower Dangan, Galway, Ireland. E-mails: thule.pham@insight-centre.org, ali.intizar@insight-centre.org

2. Insight Centre for Data Analytics, Dublin City University, Glasnevin, Dublin 9, Dublin, Ireland. E-mail: alessandra.mileo@insight-centre.org

Publisher

IOS Press

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Reference28 articles.

1. CityBench: A Configurable Benchmark to Evaluate RSP Engines Using Smart City Datasets

2. EP-SPARQL

3. Stream reasoning and complex event processing in ETALIS;Anicic;Semantic Web,2012

4. Issues in parallel execution of non-monotonic reasoning systems;Balduccini;Parallel Computing,2005

5. C-SPARQL

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1. Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design;Semantic Web;2023-05-08

2. Microblog topic identification using Linked Open Data;PLOS ONE;2020-08-11

3. Handling Impossible Derivations During Stream Reasoning;The Semantic Web;2020

4. A Distributed Approach to LARS Stream Reasoning (System paper);Theory and Practice of Logic Programming;2019-09

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