TY - GEN
T1 - A Demonstration of Lusail
AU - Mansour, Essam
AU - Abdelaziz, Ibrahim
AU - Ouzzani, Mourad
AU - Aboulnaga, Ashraf
AU - Kalnis, Panos
N1 - KAUST Repository Item: Exported on 2020-04-23
Acknowledgements: We 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.
PY - 2017/5/10
Y1 - 2017/5/10
N2 - There 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.
AB - There 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.
UR - http://hdl.handle.net/10754/625587
UR - http://dl.acm.org/citation.cfm?doid=3035918.3058731
UR - http://www.scopus.com/inward/record.url?scp=85021199474&partnerID=8YFLogxK
U2 - 10.1145/3035918.3058731
DO - 10.1145/3035918.3058731
M3 - Conference contribution
SN - 9781450341974
SP - 1603
EP - 1606
BT - Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD '17
PB - Association for Computing Machinery (ACM)
ER -