Data Science approach for a cross-disciplinary understanding of urban phenomena: Application to energy efficiency of buildings

Sylvie Servigne; Yann Gripay; Jean-Michel Deleuil; Céline Nguyen; Jacques Jay; Olivier Cavadenti; Mebrouk Radouane

doi:10.1016/j.proeng.2015.07.353

Data Science approach for a cross-disciplinary understanding of urban phenomena: Application to energy efficiency of buildings

Sylvie Servigne ¹ Yann Gripay ¹ Jean-Michel Deleuil ² Céline Nguyen ² Jacques Jay ³ Olivier Cavadenti ⁴ Mebrouk Radouane

1 BD - Base de Données

LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information

2 EVS - Environnement Ville Société

3 CETHIL - Centre de Thermique de Lyon

4 DM2L - Data Mining and Machine Learning

LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information

Abstract : Our goal is to develop theoretical and practical tools to model, explore and exploit heterogeneous data from various sources in order to understand a phenomenon. We focus on a generic model for data acquisition campaigns based on the concept of generic sensor. The concept of generic sensor is centered on acquired data and on their inherent multi-dimensional structure, to support complex domain-specific or field-oriented analysis processes. We consider that a methodological breakthrough, based on Data Science as a pivot for interdisciplinary dialog, may pave the way to deep understanding of voluminous and heterogeneous scientific data sets. Our use case concerns energy efficiency of buildings to understand relationship between physical phenomena and user behaviors. This project concern computer scientists from the LIRIS, social and urban scientists from the EVS laboratory and thermal scientists from the CETHIL laboratory. The aim of this paper is to give a synthetic presentation of our methodology and main results.

Type de document :

Article dans une revue

Procedia Engineering, Elsevier, 2015, Toward integrated modelling of urban systems, 115, pp.45-52. 〈10.1016/j.proeng.2015.07.353 〉

Domaine :

Informatique [cs] / Base de données [cs.DB]

Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01192716
Contributeur : Sylvie Servigne <>
Soumis le : jeudi 3 septembre 2015 - 13:31:07
Dernière modification le : jeudi 12 juillet 2018 - 01:16:53

Data Science approach for a cross-disciplinary understanding of urban phenomena: Application to energy efficiency of buildings

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