Computing the Stratified Semantics of Logic Programs over Big Data through Mass Parallelization

Ilias Tachmazidis, Grigoris Antoniou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)


Increasingly huge amounts of data are published on the Web, and generated from sensors and social media. This Big Data challenge poses new scientific and technological challenges and creates new opportunities - thus the increasing attention 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 stratified semantics of logic programming, equivalent to the well-founded semantics for stratified programs, can process huge amounts of data through mass parallelization. In particular, we propose and evaluate a parallel approach using the MapReduce framework. Our experimental results indicate that our approach is scalable and that stratified semantics of logic programming can be applied to billions of facts.

Original languageEnglish
Title of host publicationTheory, Practice, and Applications of Rules on the Web - 7th International Symposium, RuleML 2013, Proceedings
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783642396168
Publication statusPublished - Jul 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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