[ZDNet] BigDataFr recommends: How HelloFresh uses big data to cook up millions of custom meals

BigDataFr recommends: How HelloFresh uses big data to cook up millions of custom meals Work, commuting, child care and other activities can make time seem like a precious commodity in the modern world — and that’s before finding something to eat every evening. Admittedly, we don’t have to go hunting and foraging like our ancient […]

[La Tribune] BigDataFr recommande : La France se prépare au futur de son industrie

BigDataFr recommande : La France se prépare au futur de son industrie […] Robots et capteurs, simulation et connectivité, intégration de la culture digitale et bouleversement des modèles économiques, l’industrie se transforme en profondeur. Cette mutation est organisationnelle autant que technologique. Toutes les entreprises françaises n’ont pas encore pris conscience des changements profonds qui nécessitent […]

[arXiv] BigDataFr recommends: A European research roadmap for optimizing societal impact of big data on environment and energy efficiency

BigDataFr recommends: A European research roadmap for optimizing societal impact of big data on environment and energy efficiency […] We present a roadmap to guide European research efforts towards a socially responsible big data economy that maximizes the positive impact of big data in environment and energy efficiency. The goal of the roadmap is to […]

[HAL] BigDataFr recommends: Stream-based Reasoning for IoT Applications – Proposal of Architecture and Analysis of Challenges

BigDataFr recommends: Stream-based Reasoning for IoT Applications – Proposal of Architecture and Analysis of Challenges Abstract […] As distributed IoT applications become larger and more complex, the pure processing of raw sensor and actuation data streams becomes impractical. Instead, data streams must be fused into tangible facts and these pieces of information must be combined […]

[La Tribune] BigDataFr recommande : Data marketing : la startup Mediarithmics lève 3 millions d’euros pour s’internationaliser

BigDataFr recommande : Data marketing : la startup Mediarithmics lève 3 millions d’euros pour s’internationaliser […] Pas facile de se faire un nom dans un secteur, le marketing personnalisé, dominé par des géants américains tels que SAP, Oracle, IBM ou encore HP. Pourtant, la startup française Mediarithmics a décidé de se lancer dans le bain. […]

[Dataconomy] BigDataFr recommends: Trends Shaping Machine Learning in 2017

BigDataFr recommends: Trends Shaping Machine Learning in 2017 […] Technologies in the field of data science are progressing at an exponential rate. The introduction of Machine Learning has revolutionized the world of data science by enabling computers to classify and comprehend large data sets. Another important innovation which has changed the paradigm of the world […]

[JDN] BigDataFr recommends: L’or noir du numérique, le big data nous livre ses secrets

BigDataFr recommande: L’or noir du numérique, le big data nous livre ses secrets r […] Ce qu’on appelle communément « l’or noir du numérique » demeure une matière encore difficile à exploiter pleinement par les établissements financiers. Amenée à augmenter de manière exponentielle dans les prochaines années, cette masse d’information est continuellement alimentée par le multimédia, l’omniprésence […]

[ZDNet] BigDataFr recommande: Report shows that AI is more important to IoT than big data insights

BigDataFr recommends: Report shows that AI is more important to IoT than big data insights The problem with big data and business intelligence software is that it is reactionary and static. A recent survey from data analysis provider GlobalData showed that IoT professionals still have a heavy reliance on traditional business intelligence (BI) software. Around […]

[arXiv] BigDataFr recommends: Massively-Parallel Feature Selection for Big Data

BigDataFr recommends: Massively-Parallel Feature Selection for Big Data […] We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size). To tackle the challenges of Big Data FS PFBP partitions the data matrix both in terms of rows (samples, training examples) as well as […]