Name
Abstract | ||
|---|---|---|
A probabilistic relational database is a probability distribution over a set of deterministic relational databases (namely, possible worlds). Efficient processing of updating information in probabilistic databases is required in several applications, such as sensor networking, data cleaning. As an important class of updating probabilistic databases, conditioning refines probability distribution of possible worlds, and possibly removing some of the possible worlds based on general knowledge, such as primary key constraints, functional dependencies and others. The existing methods for conditioning are exponential over the number of variables in the probabilistic database for an arbitrary constraint. In this paper, a constraint-based conditioning algorithm is proposed by only considering the variables in the given constraint without enumerating the truth values of all the variables in the formulae of tuples. Then we prove the correctness of the algorithm. The experimental study shows our proposed algorithm is more efficient comparing the work in the literatures. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-08010-9_25 | WEB-AGE INFORMATION MANAGEMENT, WAIM 2014 |
Keywords | Field | DocType |
Database, Probabilistic data, Conditioning, Constraint | Data mining,Relational database,Computer science,Statistical relational learning,Tuple,Correctness,Functional dependency,Probability distribution,Artificial intelligence,Probabilistic logic,Machine learning,Probabilistic database | Conference |
Volume | ISSN | Citations |
8485 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Zhu | 1 | 7 | 5.52 |
| Caicai Zhang | 2 | 5 | 6.43 |
| Zhongsheng Cao | 3 | 2 | 1.37 |
| Ruiming Tang | 4 | 1 | 0.69 |
| Mengyuan Yang | 5 | 1 | 0.35 |