Planning meets Data Cleansing - Boselli, Roberto; Cesarini, Mirko; Mercorio, Fabio; Mezzanzanica, Mario (Proceedings of the International Conference on Automated Planning and Scheduling, 2014)
One of the motivations for research in data quality is to automatically identify cleansing activities, namely a sequence of actions able to cleanse a dirty dataset, which today are often developed manually by domain-experts. Here we explore the idea that AI Planning can contribute to identify data inconsistencies and automatically fix them. To this end, we formalise the concept of cost-optimal Universal Cleanser — a collection of cleansing actions for each data inconsistency — as a planning problem. We present then a motivating government application in which it has be used.