Efficient computation of the well-founded semantics over big data

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Data originating from the Web, 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 well-founded 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.

LanguageEnglish
Pages445-459
Number of pages15
JournalTheory and Practice of Logic Programming
Volume14
Issue number4-5
DOIs
Publication statusPublished - 2014

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Well-founded Semantics
Semantics
Logic programming
Nonmonotonic Reasoning
Social Media
MapReduce
Sensors
Logic Programming
Stratification
Parallelization
Predicate
Industry
Restriction
Sensor
Big data
Evaluate
Experimental Results
Arbitrary
Knowledge

Cite this

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abstract = "Data originating from the Web, 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 well-founded 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.",
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Efficient computation of the well-founded semantics over big data. / Tachmazidis, Ilias; Antoniou, Grigoris; Faber, Wolfgang.

In: Theory and Practice of Logic Programming, Vol. 14, No. 4-5, 2014, p. 445-459.

Research output: Contribution to journalArticle

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