| Orion 2.0: native support for uncertain data |
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International Conference on Management of Data
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Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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Vancouver, Canada
DEMONSTRATION SESSION: Group 1
table of contents
Pages 1239-1242
Year of Publication: 2008
ISBN:978-1-60558-102-6
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Authors
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Sarvjeet Singh
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Purdue University, West Lafayette, IN, USA
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Chris Mayfield
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Purdue University, West Lafayette, IN, USA
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Sagar Mittal
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Purdue University, West Lafayette, IN, USA
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Sunil Prabhakar
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Purdue University, West Lafayette, IN, USA
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Susanne Hambrusch
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Purdue University, West Lafayette, IN, USA
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Rahul Shah
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Purdue University, West Lafayette, IN, USA
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| Bibliometrics |
Downloads (6 Weeks): 13, Downloads (12 Months): 116, Citation Count: 4
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ABSTRACT
Orion is a state-of-the-art uncertain database management system with built-in support for probabilistic data as first class data types. In contrast to other uncertain databases, Orion supports both attribute and tuple uncertainty with arbitrary correlations. This enables the database engine to handle both discrete and continuous pdfs in a natural and accurate manner. The underlying model is closed under the basic relational operators and is consistent with Possible Worlds Semantics. We demonstrate how Orion simplifies the design and enhances the capabilities of two example applications: managing sensor data (continuous uncertainty) and inferring missing values (discrete uncertainty).
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.
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Reynold Cheng , Sarvjeet Singh , Sunil Prabhakar , Rahul Shah , Jeffrey Scott Vitter , Yuni Xia, Efficient join processing over uncertain data, Proceedings of the 15th ACM international conference on Information and knowledge management, November 06-11, 2006, Arlington, Virginia, USA
[doi> 10.1145/1183614.1183719]
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Reynold Cheng , Yuni Xia , Sunil Prabhakar , Rahul Shah , Jeffrey Scott Vitter, Efficient indexing methods for probabilistic threshold queries over uncertain data, Proceedings of the Thirtieth international conference on Very large data bases, p.876-887, August 31-September 03, 2004, Toronto, Canada
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J. E. Conway. PL/R - R Procedural Language for PostgreSQL. http://www.joeconway.com/plr/, 2008.
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S. Singh, C. Mayfield, S. Prabhakar, R. Shah, and S. Hambrusch. Indexing Uncertain Categorical Data. In IEEE 23rd Intl. Conference on Data Engineering, 2006.
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S. Singh, C. Mayfield, R. Shah, S. Prabhakar, and S. Hambrusch. Query Selectivity Estimation for Uncertain Data. In 20th Intl. Conf. on Scientific and Statistical Database Management, 2008.
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S. Singh, C. Mayfield, R. Shah, S. Prabhakar, S. Hambrusch, J. Neville, and R. Cheng. Database Support for Probabilistic Attributes and Tuples. In IEEE 24th Intl. Conference on Data Engineering, 2008.
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CITED BY 4
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Fei Xu , Kevin Beyer , Vuk Ercegovac , Peter J. Haas , Eugene J. Shekita, E = MC3: managing uncertain enterprise data in a cluster-computing environment, Proceedings of the 35th SIGMOD international conference on Management of data, June 29-July 02, 2009, Providence, Rhode Island, USA
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