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Efficient Computation of the Well-Founded Semantics over Big Data

Tachmazidis, Ilias, Antoniou, Grigoris and Faber, Wolfgang (2014) Efficient Computation of the Well-Founded Semantics over Big Data. Theory and Practice of Logic Programming, 14 (4-5). pp. 445-459. ISSN 1471-0684

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Data originating from theWeb, sensor readings and social media result in increasingly huge datasets. The so called Big Data comes with new scientific and technological challenges while creating new opportunities, hence the increasing interest in academia and industry. Traditionally, logic programming has focused on complex knowledge structures/programs, so the question arises whether and how it can work in the face of Big Data. In this paper, we examine how the well-founded semantics can process huge amounts of data through mass parallelization. More specifically, we propose and evaluate a parallel approach using the MapReduce framework. Our experimental results indicate that our approach is scalable and that wellfounded semantics can be applied to billions of facts. To the best of our knowledge, this is the first work that addresses large scale nonmonotonic reasoning without the restriction of stratification for predicates of arbitrary arity.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering
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Depositing User: Grigoris Antoniou
Date Deposited: 08 Jul 2014 15:11
Last Modified: 28 Aug 2021 19:05


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