Support for ICT entrepreneurs to match each need

French mobile phone provider Orange has developed several support programmes for ICT start-ups in Africa and the Middle East. By providing the right, tailor made support facilities it aims to enable a home-grown e-agriculture sustainable growth model for innovative, young entrepreneurs

A telecom operator in West Africa

With a presence in 18 African countries, Orange is an important telecom operator in Africa. Making its services available to small farmers is one of the company's priorities

A Spatial-based KDD Process to Better Understand the Spatiotemporal Phenomena

International audienceIn this paper, we present a knowledge discovery process ap- plied to hydrological data. To achieve this objective, we combine succes- sive methods to extract knowledge on data collected at stations located along several rivers. Firstly, data is pre processed in order to obtain different spatial proximities. Later, we apply two algorithms to extract spatiotemporal patterns and compare them. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and rivers monitoring pressure data

Finding Relevant Sequences With The Least Temporal Contradiction Measure: Application to Hydrological Data

International audienceIn this paper, we present a knowledge discovery process applied to hydrological data. To achieve this objective, we apply an algorithm to extract sequential patterns on data collected at stations located along several rivers. The data is pre-processed in order to obtain different spatial proximities and the number of patterns is estimated to highlight the influence of defined spatial relationship. We provide an objective measure of assessment, called the least temporal contradiction, to help the expert in discovering new knowledge. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and rivers monitoring pressure data

Vers des solutions adaptatives et génériques pour l'extraction de motifs intéressants dans les données

The discovery of frequent patterns is one of the problems in data mining. To better understand the influence of the data on the algorithms, we present an experimental study of data sets commonly used by the community. This study lead to a new classification of data based on edge: stable and consistent with the performance of algorithms. Despite the large number of studies and a theoretical framework for extracting interesting patterns problems, the use of these algorithms for solving problems "equivalent" is uncommon and remains difficult. Given these limitations, we propose a generic algorithm for discovering interesting patterns borders, called ABS (Adaptive Search borders), dynamically adapting its strategy to data. In addition, a generic component library C + + has been proposed to facilitate the development of software solutions for this family of problemsLa découverte de motifs fréquents est un des problèmes en fouille de données. Afin de mieux comprendre l'influence des données sur les algorithmes, nous présentons une étude expérimentale des jeux de données communément utilisés par la communauté. Cette étude permet d'aboutir à une nouvelle classification des données en fonction des bordures : stable et en accord avec les performances des algorithmes. Malgré le grand nombre de travaux et un cadre théorique des problèmes d'extraction de motifs intéressants, l'utilisation de ces algorithmes pour résoudre des problèmes "équivalents" est peu répandue et reste délicate. Face à ces limites, nous proposons un algorithme générique de découverte des bordures des motifs intéressants, appelé ABS (Adaptive borders Search), adaptant dynamiquement sa stratégie en fonction des données. De plus, une librairie générique de composants C++ a été proposée pour faciliter le développement de solutions logicielles pour cette famille de problèmes