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SPROUT2: a squared query engine for uncertain web data

Published: 12 June 2011 Publication History

Abstract

SPROUT² is a query answering system that allows users to ask structured queries over tables embedded in Web pages, over Google Fusion tables, and over uncertain tables that can be extracted from answers to Google Squared. At the core of this service lies SPROUT, a query engine for probabilistic databases. This demonstration allows users to compose and ask ad-hoc queries of their choice and also to take a tour through the system's capabilities along pre-arranged scenarios on, e.g., movie actors and directors, biomass facilities, or leveraging corporate databases.

References

[1]
AggData -- FreeData Directory. Accessed Dec 2010. \footnotesize http://www.aggdata.com/free-data.
[2]
L. Antova, T. Jansen, C. Koch, and D. Olteanu. Fast and Simple Relational Processing of Uncertain Data. In ICDE, 2008.
[3]
http://www.findacomposter.com.Accessed Dec 2010.
[4]
R. Fink and D. Olteanu. On the optimal approximation of queries using tractable propositional languages. In ICDT, 2011.
[5]
R. Fink, D. Olteanu, and S. Rath. Providing support for full relational algebra in probabilistic databases. In ICDE, 2011.
[6]
M. Penn and E. K. Zalesne. Microtrends: The Small Forces Behind Tomorrow's Big Changes. Twelve, 2007.
[7]
The SPROUT team. SPROUT: Scalable Query Processing in Uncertain Databases. Oxford University, http://www.comlab.ox.ac.uk/projects/SPROUT, 2010.

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cover image ACM Conferences
SIGMOD '11: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
June 2011
1364 pages
ISBN:9781450306614
DOI:10.1145/1989323

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2011

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Author Tags

  1. probabilistic databases
  2. web data management

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  • Demonstration

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SIGMOD/PODS '11
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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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  • (2021)Open-world probabilistic databases: Semantics, algorithms, complexityArtificial Intelligence10.1016/j.artint.2021.103474295(103474)Online publication date: Jun-2021
  • (2019)Uncertainty Annotated Databases - A Lightweight Approach for Approximating Certain AnswersProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3319887(1313-1330)Online publication date: 25-Jun-2019
  • (2018)Recent advances in querying probabilistic knowledge basesProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304652.3304779(5420-5426)Online publication date: 13-Jul-2018
  • (2018)Parallelizing Sequential Graph ComputationsACM Transactions on Database Systems10.1145/328248843:4(1-39)Online publication date: 16-Dec-2018
  • (2018)Learning From Query-AnswersACM Transactions on Database Systems10.1145/327750343:4(1-41)Online publication date: 8-Dec-2018
  • (2018)Optimal Bloom Filters and Adaptive Merging for LSM-TreesACM Transactions on Database Systems10.1145/327698043:4(1-48)Online publication date: 8-Dec-2018
  • (2018)Query Processing over Uncertain DataEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_80689(3052-3058)Online publication date: 7-Dec-2018
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