Alessandra Reale

Balancing of agricultural census data by using discrete optimization

In the case of large-scale surveys, such as a Census, data may contain errors or missing values. An automatic error correction procedure is therefore needed. We focus on the problem of restoring the consistency of agricultural data concerning cultivation areas and number of livestock, and we propose here an approach to this balancing problem based on optimization. Possible alternative models, either linear, quadratic or mixed integer, are presented. The mixed integer linear one has been preferred and used for the treatment of possibly unbalanced data records. Results on real-world Agricultural Census data show the effectiveness of the proposed approach.

Open Source Integer Linear Programming Solvers for Error Localization in Numerical Data

Error localization problems can be converted into Integer Linear Programming problems. This approach provides several advantages and guarantees to find a set of erroneous fields having minimum total cost. By doing so, each erroneous record produces an Integer Linear Programming model that should be solved. This requires the use of specific solution softwares called Integer Linear Programming solvers. Some of these solvers are available as open source software. A study on the performance of internationally recognized open source Integer Linear Programming solvers, compared to a reference commercial solver on real-world data having only numerical fields, is reported. The aim was to produce a stressing test environment for selecting the most appropriate open source solver for performing error localization in numerical data.