Working Models for Uncertain Data

By Anish Das Sarma, Omar Benjelloun, Alon Y. Halevy, Jennifer Widom. IEEE International Conference on Data Engineering, 2006

385 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/9d4286884928d59fbaf1edbca41f5c5266e42b70

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. Charu C. Aggarwal
2. Philip S. Yu
3. Ihab F. Ilyas
4. Christopher R'e
5. Kevin Chen-Chuan Chang
6. M. Franklin
7. Jian Pei
8. Mohamed A. Soliman

In Computer Networks and Communications

1. Philip S. Yu
2. Ihab F. Ilyas
3. Charu C. Aggarwal
4. Mohamed A. Soliman
5. Alon Y. Halevy
6. Kevin Chen-Chuan Chang
7. Xuemin Lin
8. M. Franklin

In Signal Processing

1. Charu C. Aggarwal
2. Ihab F. Ilyas
3. Mohamed A. Soliman
4. Jian Pei
5. Philip S. Yu
6. Xuemin Lin
7. Kevin Chen-Chuan Chang
8. W. Zhang

Citations in the corpus

• Probabilistic Skylines on Uncertain Data (2007, background): “Modelingand querying uncertain data have attracted considerable attention from database research community (see [25, 12] and the references therein).”
• Management of probabilistic data: foundations and challenges (2007, background): “The representation problem for probabilistic database is an active research topic [26, 10, 41, 6, 5], and Green and Tannen [41] observed a strong connection between representation systems for probabilistic databases and for incomplete database.”
• Principles of dataspace systems (2006, background): “Several formalisms have been proposed for uncertain databases [2, 6, 18, 29, 34, 38]).”
• A Survey of Uncertain Data Algorithms and Applications (2009, methodology): “A recent and interesting line of models for uncertain data is derived from the Trio project [16], [62], [34] at Stanford University.”
• Ranking queries on uncertain data: a probabilistic threshold approach (2008, methodology): “The uncertain data in Table 1 carries the possible world semantics [1, 16, 23 , 8]. The data can be viewed as the summary of a set of possible worlds.”
• Top-k Query Processing in Uncertain Databases (2007, background): “The structure and probabilities of possible worlds are potentially affected by probabilistic correlations among tuples, e.g., mutual exclusion of tuples that map to the same real world entity [20].”
• Efficient Top-k Query Evaluation on Probabilistic Data (2007, background): “A recent approach to manage imprecisions is with a probabilistic database, which uses probabilities to represent the uncertainty about the data [5, 6, 7, 8, 21].”