Efficient query evaluation on probabilistic databases

By Nilesh N. Dalvi, Dan Suciu. The VLDB journal, 2004

1,057 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ce56200ee7ab1ecd59c93bb0a0df5ca6190bf8b6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Luc De Raedt
2. Angelika Kimmig
3. Kevin Chen-Chuan Chang
4. Kristian Kersting
5. Aditya G. Parameswaran
6. Alon Y. Halevy
7. Bernd Gutmann
8. Adam Marcus

In Computer Networks and Communications

1. Mohamed A. Soliman
2. Ihab F. Ilyas
3. Kevin Chen-Chuan Chang
4. Xuemin Lin
5. Alon Y. Halevy
6. Jian Pei
7. Aditya G. Parameswaran
8. Nilesh N. Dalvi

In Signal Processing

1. Luc De Raedt
2. Mohamed A. Soliman
3. Ihab F. Ilyas
4. Angelika Kimmig
5. Xuemin Lin
6. Kevin Chen-Chuan Chang
7. Jian Pei
8. Aditya G. Parameswaran

Citations in the corpus

• ULDBs: databases with uncertainty and lineage (2006, background): “Acknowledgments We thank Parag Agrawal, Dan Suciu, Jeff Ullman, and the en­tire Trio group for helpful discussions, and Chris Hayworth for creating an initial prototype implementation of ULDBs.”
• Probabilistic Skylines on Uncertain Data (2007, background): “Modeling and querying uncertain data have attracted considerable attention from database research community (see [25, 12 ] and the references therein).”
• Under Consideration for Publication in Theory and Practice of Logic Programming on the Implementation of the Probabilistic Logic Programming Language Problog (neutral): “…(Poole 1993b; Poole 2000), PRISM (Sato and Kameya 2001), SLPs (Muggleton 1995), ProbView (Lakshmanan et al. 1997), CLP(BN ) (Santos Costa et al. 2003), CP-logic (Vennekens et al. 2004), Trio (Widom 2005), probabilistic Datalog (pD) (Fuhr 2000), and probabilistic databases (Dalvi and Suciu 2004).”
• Crowdsourced Data Management: Industry and Academic Perspectives (2015, background): “Dalvi and Suciu explore efficient queries over probabilistic databases [62].”
• MCDB: a monte carlo approach to managing uncertain data (2008, methodology): “Even relatively simple queries can result in #P complexity for query evaluation [11], and aggregation queries such as SUM and AVG, which are fundamental to OLAP and BI processing, pose significant challenges [25].”
• Top-k Query Processing in Uncertain Databases (2007, methodology): “According to most of these models, tuples have membership probability, e.g., based on data source reliability [13], or fuzzyquery predicates, as addressed in [ 18 ].”
• Parameter Learning in Probabilistic Databases: A Least Squares Approach (2008, methodology): “Probabilistic databases [3, 4] associate probabilities to facts, indicating the probabilities with which these facts hold.”
• Management of probabilistic data: foundations and challenges (2007, background): “A query q is said to be without self-joins if each relational symbol occurs at most once in the query body [21, 20].”