Name
Abstract | ||
|---|---|---|
The paper is concerned with the problem of automatic detection and correction of errors into massive datasets. As customary, erroneous data records are detected by formulating a set of rules. Such rules are here encoded into linear inequalities. This allows to check the set of rules for inconsistencies and redundancies by using a polyhedral mathematics approach. Moreover, it allows to correct erroneous data records by introducing the minimum changes through an integer linear programming approach. Results of a particularization of the proposed procedure to a real-world case of census data correction are reported. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1080/10556780512331318281 | OPTIMIZATION METHODS & SOFTWARE |
Keywords | Field | DocType |
data correction,inconsistency localization,massive datasets | Data mining,Statistic,Computer science,Error detection and correction,Integer programming,Linear inequality,Data records | Journal |
Volume | Issue | ISSN |
20 | 2-3 | 1055-6788 |
Citations | PageRank | References |
4 | 0.52 | 13 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Renato Bruni | 1 | 127 | 15.79 |