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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

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.

LanguageEnglish
Title of host publicationTheory, Practice, and Applications of Rules on the Web - 7th International Symposium, RuleML 2013, Proceedings
PublisherSpringer Verlag
Pages188-202
Number of pages15
ISBN (Print)9783642396168
DOIs
Publication statusPublished - Jul 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8035
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Logic programming
Logic Programs
Parallelization
Logic Programming
Semantics
Computing
Well-founded Semantics
Social Media
MapReduce
Sensors
Industry
Sensor
Big data
Evaluate
Experimental Results

Cite this

Tachmazidis, I., & Antoniou, G. (2013). Computing the Stratified Semantics of Logic Programs over Big Data through Mass Parallelization. In Theory, Practice, and Applications of Rules on the Web - 7th International Symposium, RuleML 2013, Proceedings (pp. 188-202). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8035). Springer Verlag. https://doi.org/10.1007/978-3-642-39617-5_18
Tachmazidis, Ilias ; Antoniou, Grigoris. / Computing the Stratified Semantics of Logic Programs over Big Data through Mass Parallelization. Theory, Practice, and Applications of Rules on the Web - 7th International Symposium, RuleML 2013, Proceedings. Springer Verlag, 2013. pp. 188-202 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{86019229643d4c368ec643b5a1c1242a,
title = "Computing the Stratified Semantics of Logic Programs over Big Data through Mass Parallelization",
abstract = "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.",
author = "Ilias Tachmazidis and Grigoris Antoniou",
year = "2013",
month = "7",
doi = "10.1007/978-3-642-39617-5_18",
language = "English",
isbn = "9783642396168",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "188--202",
booktitle = "Theory, Practice, and Applications of Rules on the Web - 7th International Symposium, RuleML 2013, Proceedings",

}

Tachmazidis, I & Antoniou, G 2013, Computing the Stratified Semantics of Logic Programs over Big Data through Mass Parallelization. in Theory, Practice, and Applications of Rules on the Web - 7th International Symposium, RuleML 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8035, Springer Verlag, pp. 188-202. https://doi.org/10.1007/978-3-642-39617-5_18

Computing the Stratified Semantics of Logic Programs over Big Data through Mass Parallelization. / Tachmazidis, Ilias; Antoniou, Grigoris.

Theory, Practice, and Applications of Rules on the Web - 7th International Symposium, RuleML 2013, Proceedings. Springer Verlag, 2013. p. 188-202 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8035).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Computing the Stratified Semantics of Logic Programs over Big Data through Mass Parallelization

AU - Tachmazidis, Ilias

AU - Antoniou, Grigoris

PY - 2013/7

Y1 - 2013/7

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84880989849&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-39617-5_18

DO - 10.1007/978-3-642-39617-5_18

M3 - Conference contribution

SN - 9783642396168

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 188

EP - 202

BT - Theory, Practice, and Applications of Rules on the Web - 7th International Symposium, RuleML 2013, Proceedings

PB - Springer Verlag

ER -

Tachmazidis I, Antoniou G. Computing the Stratified Semantics of Logic Programs over Big Data through Mass Parallelization. In Theory, Practice, and Applications of Rules on the Web - 7th International Symposium, RuleML 2013, Proceedings. Springer Verlag. 2013. p. 188-202. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-39617-5_18