Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
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Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
Scalable SPARQL Querying using Path Partitioning
Proceedings of the 31st IEEE International Conference on Data Engineering (ICDE2015), April 13-16 2015, Seoul, Korea.
Buwen Wu†#, Yongluan Zhou#, Pingpeng Yuan†, Ling Liu§, Hai Jin†
† SCTS/CGCL, Huazhong University of Science and Technology, Wuhan, China
# University of Southern Denmark
§ Georgia Institute of Technology
The emerging need for conducting complex analysis over big RDF datasets calls for scale-out solutions that can harness a computing cluster to process big RDF datasets. Queries over RDF data often involve complex self-joins, which would be very expensive to run if the data are not carefully partitioned across the cluster and hence distributed joins over massive amount of data are necessary. Existing RDF data partitioning methods can nicely localize simple queries but still need to resort to expensive distributed joins for more complex queries. In this paper, we propose a new data partitioning approach that takes use of the rich structural information in RDF datasets and minimizes the amount of data that have to be joined across different computing nodes. We conduct an extensive experimental study using two popular RDF benchmark data and one real RDF dataset that contain up to billions of RDF triples. The results indicate that our approach can produce a balanced and low redundant data partitioning scheme that can avoid or largely reduce the cost of distributed joins even for very complicated queries. In terms of query execution time, our approach can outperform the state-of-the-art methods by orders of magnitude.
FULL PAPER: pdf