Automatic Synthesis of Data Cleansing Activities

Data cleansing is growing in importance among both public and private organisations, mainly due to the relevant amount of data exploited for supporting decision making processes. This paper is aimed to show how model-based verification algorithms (namely, model checking) can contribute in addressing data cleansing issues, furthermore a new benchmark problem focusing on the labour market dynamic is introduced. The consistent evolution of the data is checked using a model defined on the basis of domain knowledge. Then, we formally introduce the concept of universal cleanser, i.e. an object which summarises the set of all cleansing actions for each feasible data inconsistency (according to a given consistency model), then providing an algorithm which synthesises it. The universal cleanser can be seen as a repository of corrective interventions useful to develop cleansing routines. We applied our approach to a dataset derived from the Italian labour market data, making the whole dataset and outcomes publicly available to the community, so that the results we present can be shared and compared with other techniques.