What You Always Wanted to Know About Datalog (And Never Dared to Ask)
IEEE Transactions on Knowledge and Data Engineering
RDFPeers: a scalable distributed RDF repository based on a structured peer-to-peer network
Proceedings of the 13th international conference on World Wide Web
Anytime Query Answering in RDF through Evolutionary Algorithms
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
YARS2: a federated repository for querying graph structured data from the web
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Continuous RDF query processing over DHTs
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Foundations of SPARQL query optimization
Proceedings of the 13th International Conference on Database Theory
PigSPARQL: mapping SPARQL to Pig Latin
Proceedings of the International Workshop on Semantic Web Information Management
High-performance computing applied to semantic databases
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Robust runtime optimization and skew-resistant execution of analytical SPARQL queries on pig
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
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Science is increasingly motivated by the need to process larger quantities of data. It is facing severe challenges in data collection, management, and processing, so much so that the computational demands of "data scaling" are competing with, and in many fields surpassing, the traditional objective of decreasing processing time. Example domains with large datasets include astronomy, biology, genomics, climate/weather, and material sciences. This paper presents a real-world use case in which we wish to answer queries provided by domain scientists in order to facilitate discovery of relevant science resources. The problem is that the metadata for these science resources is very large and is growing quickly, rapidly increasing the need for a data scaling solution. We propose a system -- SGEM -- designed for answering graph-based queries over large datasets on cluster architectures, and we report early results for our current capability.