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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CITATION STYLE
Boselli, R., Cesarini, M., Mercorio, F., & Mezzanzanica, M. (2017). An AI Planning System for Data Cleaning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10536 LNAI, pp. 349–353). Springer Verlag. https://doi.org/10.1007/978-3-319-71273-4_29
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