Big data, individu et société

International audienc

Towards quality oriented data warehouse usage and evolution

As a decision support information system, a data warehouse must provide high level quality of data and services. In the DWQ project (Foundations of Data Warehouse Quality), we have proposed how semantically rich meta-information of a data warehouse can be stored in a metadata repository. This sfutic representation of the various perspectives of data warehouse components and their linkage to quality factors is complemented by an operutionul methodology on how to use these quality factors and achieve the quality goals of the users. This approach is an extension of the Goal-Question-Metric (GQM) approach, based on the idea that a quality goal is operationally defined over a concrete set of questions, i.e., algorithmic steps. The proposed approach covers the full lifecycle of the data warehouse, allows capturing the interrelationships between different quality factors and helps the interested user to organize them in order to fulfill specific quality goals. Furthermore, we prove how the quality management of the data warehouse can guide the process of data warehouse evolution, by tracking the interrelationships between the components of the data warehouse. Finally, we present a case study, as a proof of concept for the proposed methodology.Information System

Agriculture et Big Data

National audienceSince the end of the nineties, the technological offering (successively GPS, embedded sensors, satellite imagery, UAV and wireless sensors) has rehabilitated intra-parcel crop monitoring. But these new high technicality approaches require more and more the intervention of service companies for data collection. This should generate in the next years a considerable amount of agronomical Big Data, allowing refining crop growth and decision support models.Depuis la fin des années 1990, l'offre technologique (successivement GPS, capteurs embarqués, imagerie satellitaire, drones, et capteurs sans fil) a réhabilité l'observation intra-parcellaire des cultures. Mais ces nouvelles approches, de technicité croissante, supposent de plus en plus l'intervention de sociétés de service pour la collecte des données. Il devrait s'en suivre, dans les années à venir, la constitution d'un formidable vivier de "Big Data" agronomiques permettant d'affiner les modèles de croissance des cultures et de décision