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MayBMS: a probabilistic database management system

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International Conference on Management of Data archive
Proceedings of the 35th SIGMOD international conference on Management of data table of contents

Providence, Rhode Island, USA

DEMONSTRATION SESSION: Demonstration session: group B table of contents

Pages 1071-1074

Year of Publication: 2009

ISBN:978-1-60558-551-2

Authors

Jiewen Huang	University of Oxford, Oxford, United Kingdom
Lyublena Antova	Cornell University, Ithaca, USA
Christoph Koch	Cornell University, Ithaca, USA
Dan Olteanu	University of Oxford, Oxford, United Kingdom

Sponsors

ACM: Association for Computing Machinery
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

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ABSTRACT

MayBMS is a state-of-the-art probabilistic database management system which leverages the strengths of previous database research for achieving scalability. As a proof of concept for its ease of use, we have built on top of MayBMS a Web-based application that offers NBA-related information based on what-if analysis of team dynamics using data available at www.nba.com.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Lyublena Antova , Thomas Jansen , Christoph Koch , Dan Olteanu, Fast and Simple Relational Processing of Uncertain Data, Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, p.983-992, April 07-12, 2008 [doi>10.1109/ICDE.2008.4497507]
	2	Paul Dagum , Richard Karp , Michael Luby , Sheldon Ross, An Optimal Algorithm for Monte Carlo Estimation, SIAM Journal on Computing, v.29 n.5, p.1484-1496, March 2000 [doi>10.1137/S0097539797315306]
	3	Christoph Koch , Dan Olteanu, Conditioning probabilistic databases, Proceedings of the VLDB Endowment, v.1 n.1, August 2008 [doi>10.1145/1453856.1453894]
	4	]]C. Koch, D. Olteanu, L. Antova, and J. Huang. MayBMS: A Probabilistic Database System. User Manual. http://maybms.sourceforge.net/manual/,2009.
	5	Dan Olteanu , Jiewen Huang , Christoph Koch, SPROUT: Lazy vs. Eager Query Plans for Tuple-Independent Probabilistic Databases, Proceedings of the 2009 IEEE International Conference on Data Engineering, p.640-651, March 29-April 02, 2009 [doi>10.1109/ICDE.2009.123]