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.
| Language | English |
|---|---|
| Pages | 445-459 |
| Number of pages | 15 |
| Journal | Theory and Practice of Logic Programming |
| Volume | 14 |
| Issue number | 4-5 |
| DOIs | |
| Publication status | Published - 2014 |
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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 journal › Article
TY - JOUR
T1 - Efficient computation of the well-founded semantics over big data
AU - Tachmazidis, Ilias
AU - Antoniou, Grigoris
AU - Faber, Wolfgang
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Big Data
KW - MapReduce Framework
KW - Well-Founded Semantics
UR - http://www.scopus.com/inward/record.url?scp=84904606010&partnerID=8YFLogxK
U2 - 10.1017/S1471068414000131
DO - 10.1017/S1471068414000131
M3 - Article
VL - 14
SP - 445
EP - 459
JO - Theory and Practice of Logic Programming
T2 - Theory and Practice of Logic Programming
JF - Theory and Practice of Logic Programming
SN - 1471-0684
IS - 4-5
ER -