UPC performance and potential: a NPB experimental study
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Computer Architecture, Fourth Edition: A Quantitative Approach
Computer Architecture, Fourth Edition: A Quantitative Approach
Parallel Programming in C with MPI and OpenMP
Parallel Programming in C with MPI and OpenMP
Survey on Parallel Programming Model
NPC '08 Proceedings of the IFIP International Conference on Network and Parallel Computing
Developing parallel programs: A design-oriented perspective
IWMSE '09 Proceedings of the 2009 ICSE Workshop on Multicore Software Engineering
Cross ontology query answering on the semantic web: an initial evaluation
Proceedings of the fifth international conference on Knowledge capture
Discovering and Maintaining Links on the Web of Data
ISWC '09 Proceedings of the 8th International Semantic Web Conference
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Linked Data
Introduction to linked data and its lifecycle on the web
RW'13 Proceedings of the 9th international conference on Reasoning Web: semantic technologies for intelligent data access
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The Linked Open Data cloud consists of more than 26 billion triples, of which less than 3% are links between knowledge bases. However, such links play a central role in key tasks such as cross-ontology question answering, large-scale inferencing and link-based traversal query execution models. The mere size of the Linked Data Cloud makes manual linking impossible. Consequently, Link Discovery Frameworks have been developed over the last years with the aim of providing means to detect links between knowledge bases automatically. Yet, even the current runtime-optimized frameworks for linking lead to unacceptable runtimes when presented with very large datasets. This paper addresses the time complexity of Link Discovery on very large datasets by presenting and evaluating the parallelization of the time-optimized LIMES framework by means of the MapReduce paradigm.