Large amounts of data are collected by public administrations and healthcare organizations, the integration of the data scattered in several information systems can facilitate the comprehension of complex scenarios and support the activities of decision makers. Unfortunately, the quality of information system archives is very poor, as widely stated by the existing literature. Data cleansing is one of the most frequently used data improvement technique. Data can be cleansed in several ways, the optimal choice however is strictly dependent on the integration and analysis processes to be performed. Therefore, the design of a data analysis process should consider in a holistic way the data integration, cleansing, and analysis activities. However, in the existing literature, the data integration and cleansing issues have been mostly addressed in isolation. In this paper we describe how a model based cleansing framework is extended to address also integration activities. The combined approach facilitates the rapid prototyping, development, and evaluation of data pre-processing activities. Furthermore, the combined use of formal methods and visualization techniques strongly empower the data analyst which can effectively evaluate how cleansing and integration activities can affect the data analysis. An example focusing on labour and healthcare data integration is showed.
Boselli, R., Cesarini, M., Mercorio, F., Mezzanzanica, M. (2014). A Policy-Based Cleansing and Integration Framework for Labour and Healthcare Data. In A. Holzinger, I. Jurisica (a cura di), Interactive Knowledge Discovery and Data Mining in Biomedical Informatics. State-of-the-Art and Future Challenges (pp. 141-168). Berlin Heidelberg : Springer Verlag [10.1007/978-3-662-43968-5_8].
A Policy-Based Cleansing and Integration Framework for Labour and Healthcare Data
Boselli, R;Cesarini, M;Mercorio, F;Mezzanzanica, M.
2014
Abstract
Large amounts of data are collected by public administrations and healthcare organizations, the integration of the data scattered in several information systems can facilitate the comprehension of complex scenarios and support the activities of decision makers. Unfortunately, the quality of information system archives is very poor, as widely stated by the existing literature. Data cleansing is one of the most frequently used data improvement technique. Data can be cleansed in several ways, the optimal choice however is strictly dependent on the integration and analysis processes to be performed. Therefore, the design of a data analysis process should consider in a holistic way the data integration, cleansing, and analysis activities. However, in the existing literature, the data integration and cleansing issues have been mostly addressed in isolation. In this paper we describe how a model based cleansing framework is extended to address also integration activities. The combined approach facilitates the rapid prototyping, development, and evaluation of data pre-processing activities. Furthermore, the combined use of formal methods and visualization techniques strongly empower the data analyst which can effectively evaluate how cleansing and integration activities can affect the data analysis. An example focusing on labour and healthcare data integration is showed.
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