Hexastore: sextuple indexing for semantic web data management
Proceedings of the VLDB Endowment
Scalable Semantics - The Silver Lining of Cloud Computing
ESCIENCE '08 Proceedings of the 2008 Fourth IEEE International Conference on eScience
SW-Store: a vertically partitioned DBMS for Semantic Web data management
The VLDB Journal — The International Journal on Very Large Data Bases
Scalable join processing on very large RDF graphs
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
RDFKB: efficient support for RDF inference queries and knowledge management
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Scalable Distributed Reasoning Using MapReduce
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Storage and Retrieval of Large RDF Graph Using Hadoop and MapReduce
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
The RDF-3X engine for scalable management of RDF data
The VLDB Journal — The International Journal on Very Large Data Bases
Optimizing joins in a map-reduce environment
Proceedings of the 13th International Conference on Extending Database Technology
Relational nested optional join for efficient semantic web query processing
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
SPARQL basic graph pattern processing with iterative MapReduce
Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud
A comparison of join algorithms for log processing in MaPreduce
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Data Intensive Query Processing for Large RDF Graphs Using Cloud Computing Tools
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
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Existing solutions for answering SPARQL queries in a shared-nothing environment using MapReduce failed to fully explore the substantial scalability and parallelism of the computing framework. In this paper, we propose a cost model based RDF join processing solution using MapReduce to minimize the query responding time as much as possible. After transforming a SPARQL query into a sequence of MapReduce jobs, we propose a novel index structure, called All Possible Join tree (APJ-tree), to reduce the searching space for the optimal execution plan of a set of MapReduce jobs. To speed up the join processing, we employ hybrid join and bloom filter for performance optimization. Extensive experiments on real data sets proved the effectiveness of our cost model. Our solution has as much as an order of magnitude time saving compared with the state of art solutions.