AAAI Publications, Twenty-Fourth International Conference on Automated Planning and Scheduling

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Planning meets Data Cleansing
Roberto Boselli, Mirko Cesarini, Fabio Mercorio, Mario Mezzanzanica

Last modified: 2014-05-11


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.


Data Quality; Data Cleansing; Government Application

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