Planning meets Data Cleansing

Roberto Boselli · Mirko Cesarini · Fabio Mercorio · Mario Mezzanzanica

24 citations0/0 files storedProceedings of the International Conference on Automated Planning and Scheduling

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Data Quality and Management AI-based Problem Solving and Planning Machine Learning and Data Classification

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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2013NADEEF 2009On approximating optimum repairs for functional dependency violations 2010Towards certain fixes with editing rules and master data 2013The LLUNATIC data-cleaning framework 2013Don't be SCAREd 2011Towards certain fixes with editing rules and master data 2016Introduction to information quality 2016Introduction to Information Quality

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