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
Boselli, R., Cesarini, M., Mercorio, F., Mezzanzanica, M. (2014). Planning meets Data Cleansing. In Proceedings of The 24th International Conference on Automated Planning and Scheduling (ICAPS) (pp.439-443).
Planning meets Data Cleansing
BOSELLI, ROBERTO;CESARINI, MIRKO;MERCORIO, FABIO;MEZZANZANICA, MARIO
2014
Abstract
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
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