KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/625587
MetadataShow full item record
AbstractThere has been a proliferation of datasets available as interlinked RDF data accessible through SPARQL endpoints. This has led to the emergence of various applications in life science, distributed social networks, and Internet of Things that need to integrate data from multiple endpoints. We will demonstrate Lusail; a system that supports the need of emerging applications to access tens to hundreds of geo-distributed datasets. Lusail is a geo-distributed graph engine for querying linked RDF data. Lusail delivers outstanding performance using (i) a novel locality-aware query decomposition technique that minimizes the intermediate data to be accessed by the subqueries, and (ii) selectivityawareness and parallel query execution to reduce network latency and to increase parallelism. During the demo, the audience will be able to query actually deployed RDF endpoints as well as large synthetic and real benchmarks that we have deployed in the public cloud. The demo will also show that Lusail outperforms state-of-the-art systems by orders of magnitude in terms of scalability and response time.
CitationMansour E, Abdelaziz I, Ouzzani M, Aboulnaga A, Kalnis P (2017) A Demonstration of Lusail. Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD ’17. Available: http://dx.doi.org/10.1145/3035918.3058731.
SponsorsWe would like to thank Michel Dumontier, scientific director for Bio2RDF project, for the fruitful discussions regarding using Lusail as a federated engine for querying Bio2RDF datasets and for providing us with the query logs.
Conference/Event name2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017