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4 luglio 2013 – E’ stato premiato con il “Best Paper Award” il lavoro CRISP presentato alla conferenza internazionale SouthCHI 2013, tenutasi dal 1 al 4 luglio a Maribor, Slovenia.

La Conference

SouthCHI è la conferenza internazionale che affronta il tema della Human-Computer Interaction (HCI). Ha sede itinerante nei paesi sud europei / mediterranei e si svolge ogni due anni. > southchi.org.

Il lavoro premiato, ad opera dei ricercatori CRISP Roberto Boselli, Mirko Cesarini, Fabio Mercorio, e il Direttore Scientifico Mario Mezzanzanica, è stato presentato all’interno della sessione speciale HCI-KDD@SouthCHISpecial Session on Human-Computer Interaction & Knowledge Discovery (SS-HCI-KDD). > www.southchi.org/node/39

Il lavoro premiato “Best Paper Award”

R. Boselli, M. Cesarini, F. Mercorio, M. Mezzanzanica

Inconsistency Knowledge Discovery for Longitudinal Data Management: A Model-Based Approach

Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data (pp. 183-194). Springer Berlin Heidelberg, 2013

Abstract > LINK

In the last years, the growing diffusion of IT-based services has given a rise to the use of huge masses of data. However, using data for analytical and decision making purposes requires to perform several tasks, e.g. data cleansing, data filtering, data aggregation and synthesis, etc. Tools and methodologies empowering people are required to appropriately manage the (high) complexity of large datasets.

This paper proposes the multidimensional RDQA, an enhanced version of an existing model-based data verification technique, that can be used to identify, extract, and classify data inconsistencies on longitudinal data. Specifically, it discovers fine grained information about the data inconsistencies and it uses a multidimensional visualisation technique for showing them. The enhanced RDQA supports and empowers the users in the task of assessing and improving algorithms and solutions for data analysis, especially when large datasets are considered.

The proposed technique has been applied on a real-world dataset derived from the Italian labour market domain, which we made publicly available to the community.