@conference {11,
	title = {Distributed OWL EL Reasoning: The Story So Far},
	booktitle = {Proceedings of the 10th International Workshop on Scalable Semantic Web Knowledge Base Systems, Riva Del Garda, Italy},
	volume = {1261},
	year = {2014},
	month = {10/2014},
	pages = {61-76},
	publisher = {CEUR-WS.org},
	organization = {CEUR-WS.org},
	address = {Riva del Garda, Italy},
	abstract = {<p>Automated generation of axioms from streaming data, such as traffic and text, can result in very large ontologies that single machine reasoners cannot handle. Reasoning with large ontologies requires distributed solutions. Scalable reasoning techniques for RDFS, OWL Horst and OWL 2 RL now exist. For OWL 2 EL, several distributed reasoning approaches have been tried, but are all perceived to be inefficient. We analyze this perception. We analyze completion rule based distributed approaches, using different characteristics, such as dependency among the rules, implementation optimizations, how axioms and rules are distributed. We also present a distributed queue approach for the classification of ontologies in description logic EL+ (fragment of OWL 2 EL).</p>
},
	keywords = {Distributed Reasoning, OWL EL, Scalability},
	author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti},
	editor = {Thorsten Liebig and Achille Fokoue}
}
@conference {10,
	title = {DistEL: A Distributed EL+ Ontology Classifier},
	booktitle = {Proceedings of the 9th International Workshop on Scalable Semantic Web Knowledge Base Systems, co-located with the International Semantic Web Conference (ISWC 2013)},
	volume = {1046},
	year = {2013},
	month = {10/2013},
	pages = {17-32},
	publisher = {CEUR-WS.org},
	organization = {CEUR-WS.org},
	address = {Sydney, Australia},
	abstract = {OWL 2 EL ontologies are used to model and reason over data from diverse domains such as biomedicine, geography and road traffic. Data in these domains is increasing at a rate quicker than the increase in main memory and computation power of a single machine. Recent efforts in OWL reasoning algorithms lead to the decrease in classification time from several hours to a few seconds even for large ontologies like SNOMED CT. This is especially true for ontologies in the description logic EL+ (a fragment of the OWL 2 EL profile). Reasoners such as Pellet, Hermit, ELK etc. make an assumption that the ontology would fit in the main memory, which is unreasonable given projected increase in data volumes. Increase in the data volume also necessitates an increase in the computation power. This lead us to the use of a distributed system, so that memory and computation requirements can be spread across machines. We present a distributed system for the classification of EL+ ontologies along with some results on its scalability and performance.},
	keywords = {Classification, DistEL, Distributed Reasoning, EL+, OWL, Scalability},
	author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti},
	editor = {Thorsten Liebig and Achille Fokoue}
}
@conference {159,
	title = {D-SPARQ: Distributed, Scalable and Efficient RDF Query Engine},
	booktitle = {Proceedings of the ISWC 2013 Posters \& Demonstrations Track},
	volume = {1035},
	year = {2013},
	pages = {261{\textendash}264},
	publisher = {CEUR-WS.org},
	organization = {CEUR-WS.org},
	address = {Sydney, Australia},
	abstract = {<p class="rtejustify">We present D-SPARQ, a distributed RDF query engine that combines the MapReduce processing framework with a NoSQL distributed data store, MongoDB. The performance of processing SPARQL queries mainly depends on the efficiency of handling the join operations between the RDF triple patterns. Our system features two unique characteristics that enable efficiently tackling this challenge: 1) Identifying specific patterns of the input queries that enable improving the performance by running different parts of the query in a parallel mode. 2) Using the triple selectivity information for reordering the individual triples of the input query within the identified query patterns. The preliminary results demonstrate the scalability and efficiency of our distributed RDF query engine.</p>
},
	keywords = {D-SPARQ, Distributed Querying, Scalable RDF querying, SPARQL},
	url = {http://ceur-ws.org/Vol-1035/iswc2013_poster_21.pdf},
	author = {Raghava Mutharaju and Sherif Sakr and Alessandra Sala and Pascal Hitzler},
	editor = {Eva Blomqvist and Tudor Groza}
}
