Abstract
The emergence of widely available connected devices is perceived as the promise of new added-value services. Companies can now gather, often in real time, huge amounts of data about their customers’ habits. Seemingly, all they have to do is to mine these raw data in order to discover the profiles of their users and their needs.
Stemming from an industrial experience, this paper, however, shows that things are not that simple. It appears that, even in an exploratory data mining phase, the usual data cleaning and preprocessing steps are a long shot from being adequate. The rapid deployment of connected devices indeed introduces its own series of problems. The paper shares the pitfalls encountered in a project aiming at enhancing the cooking habits and presents some hard learnt lessons of general import.
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Notes
- 1.
We used the TAAABLE ontology [6], which is the most encompassing one for analyzing nutrition and food in general.
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Meftah, S., Cornuéjols, A., Dibie, J., Sicard, M. (2017). Data Collection and Analysis of Usages from Connected Objects: Some Lessons. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10351. Springer, Cham. https://doi.org/10.1007/978-3-319-60045-1_27
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DOI: https://doi.org/10.1007/978-3-319-60045-1_27
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