Data Cleaning represents a crucial and error prone activity in KDD that might have unpredictable effects on data analytics, affecting the believability of the whole KDD process. In this paper we describe how a bridge between AI Planning and Data Quality communities has been made, by expressing both the data quality and cleaning tasks in terms of AI planning. We also report a real-life application of our approach.
Boselli, R., Cesarini, M., Mercorio, F., Mezzanzanica, M. (2017). An AI Planning System for Data Cleaning. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III (pp.349-353). Springer Verlag [10.1007/978-3-319-71273-4_29].
An AI Planning System for Data Cleaning
Boselli, R;Cesarini, M;Mercorio, F
;Mezzanzanica, M
2017
Abstract
Data Cleaning represents a crucial and error prone activity in KDD that might have unpredictable effects on data analytics, affecting the believability of the whole KDD process. In this paper we describe how a bridge between AI Planning and Data Quality communities has been made, by expressing both the data quality and cleaning tasks in terms of AI planning. We also report a real-life application of our approach.
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