Error correction for massive datasets
Renato Bruni · Optimization methods & software · 2005
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.