[HAL] BigDataFr recommends: Developing a Smart Service System to Enrich Bike Riders’ Experience

BigDataFr recommends: Developing a Smart Service System to Enrich Bike Riders’ Experience Abstract […] Social sensing via mobile apps complements physical sensing (e.g., IoT) by substantially extending the horizon we know about our daily life in real time. This paper discusses how we can integrate physical and social sensing to enable better and smarter services, […]

[Dataconomy] BigDataFr recommends: Three signs you might be experiencing a NoSQL hangover

BigDataFr recommends: Three signs you might be experiencing a NoSQL hangover […] Selecting a database technology to build your new application on is often a complex and even stressful process. While the business use case for the application is pretty straightforward, the nuances of the data platform that will power it are often much less […]

[arXiv] BigDataFr recommends: Big Data vs. complex physical models: a scalable inference algorithm

BigDataFr recommends: Big Data vs. complex physical models: a scalable inference algorithm […] The data torrent unleashed by current and upcoming instruments requires scalable analysis methods. Machine Learning approaches scale well. However, separating the instrument measurement from the physical effects of interest, dealing with variable errors, and deriving parameter uncertainties is usually an after-thought. Classic […]

[La Tribune] BigDataFr recommande : Agriculture digitale : comment Atos et TerraNIS tirent ensemble profit des satellites

BigDataFr recommande : Agriculture digitale : comment Atos et TerraNIS tirent ensemble profit des satellites […] Le potentiel du big data issu de l’observation de la Terre par les satellites Sentinel, fourni en open source par l’Agence spatiale européenne, attire les grands des services numériques vers des terrains inexplorés. Jusqu’à l’agriculture, où l’arrivée du digital […]

[Dataconomy] BigDataFr recommends: Big Data’s Potential For Disruptive Innovation

BigDataFr recommends: Big Data’s Potential For Disruptive Innovation […] An innovation that creates a new value network and market, and disrupts an existing market and value network by displacing the leading, highly established alliances, products and firms is known as Disruptive Innovation. Clayton M. Christensen and his coworkers defined and analyzed this phenomenon in the […]

[Datasciencecentral] BigDataFr recommends: Embracing Conflict to Fuel Digital Innovation

BigDataFr recommends: Embracing Conflict to Fuel Digital Innovation […] When talking to clients about their business goals, most business executives are pretty clear as to what they want to accomplish, such as reducing customer churn or reducing inventory costs or improving quality of care or improving product line profitability. But these “one dimensional” business initiatives […]

[arXiv] BigDataFr recommends: A K-means clustering algorithm for multivariate big data with correlated components

BigDataFr recommends: A K-means clustering algorithm for multivariate big data with correlated components […] Common clustering algorithms require multiple scans of all the data to achieve convergence, and this is prohibitive when large databases, with millions of data, must be processed. Some algorithms to extend the popular K-means method to the analysis of big data […]

[Datasciencecentral] BigDataFr recommends: What tomorrow’s business leaders need to know about Machine Learning?

BigDataFr recommends: What tomorrow’s business leaders need to know about Machine Learning? […] Sometimes I write a blog just to formulate and organize a point of view, and I think it’s time that I pull together the bounty of excellent information about Machine Learning. This is a topic with which business leaders must become comfortable, […]