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  • Long ChengLong Cheng
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  • Knowledge SystemsKnowledge-Based Systems

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Massively Parallel Reasoning under the Well-Founded Semantics using X10

Ilias TachmazidisIlias Tachmazidis,  Long ChengLong Cheng,  Spyros KotoulasSpyros Kotoulas,  Grigoris AntoniouGrigoris Antoniou,  Tomas E WardTomas E Ward

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Ilias Tachmazidis, Long Cheng, Spyros Kotoulas, Grigoris Antoniou, Tomas E Ward
Massively Parallel Reasoning under the Well-Founded Semantics using X10
Proc. 26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'14), 162-169, November 2014. IEEE

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• KurzfassungAbstract
  Academia and industry are investigating novel approaches for processing vast amounts of data coming from enterprises, the Web, social media and sensor readings in an area that has come to be known as Big Data. Logic programming has traditionally focused on complex knowledge structures/programs. The question arises whether and how it can be applied in the context of Big Data. In this paper, we study how the well-founded semantics can be computed over huge amounts of data using mass parallelization. Specifically, we propose and evaluate a parallel approach based on the X10 programming language. Our experiments demonstrate that our approach has the ability to process up to 1 billion facts within minutes.
• Projekt:Project: DIAMOND
• Forschungsgruppe:Research Group: Knowledge SystemsKnowledge-Based Systems

@inproceedings{TCKAW2014,
  author    = {Ilias Tachmazidis and Long Cheng and Spyros Kotoulas and Grigoris
               Antoniou and Tomas E Ward},
  title     = {Massively Parallel Reasoning under the Well-Founded Semantics
               using X10},
  booktitle = {Proc. 26th {IEEE} International Conference on Tools with
               Artificial Intelligence (ICTAI'14)},
  publisher = {IEEE},
  year      = {2014},
  month     = {November},
  pages     = {162-169}
}