Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICTAI.2014.33 | ICTAI |
Keywords | DocType | ISSN |
well-founded semantics, x10, big data, mass parallelization | Conference | 1082-3409 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ilias Tachmazidis | 1 | 48 | 11.44 |
| Long Cheng | 2 | 91 | 16.99 |
| Spyros Kotoulas | 3 | 590 | 46.46 |
| Grigoris Antoniou | 4 | 2401 | 190.28 |
| Tomas E. Ward | 5 | 104 | 19.10 |