BigDataFr recommends: Improving Viability of Electric Taxis by Taxi Service Strategy Optimization: A Big Data Analysis of New York City […] Subjects: Computers and Society (cs.CY) Electrification of transportation is critical for a low-carbon society. In particular, public vehicles (e.g., taxis) provide a crucial opportunity for electrification. Despite the benefits of eco-friendliness and energy efficiency, […]
Documentation
[HAL] BigDataFr recommends: HeatPipe: High Throughput, Low Latency Big Data Heatmap with Spark Streaming
BigDataFr recommends: HeatPipe: High Throughput, Low Latency Big Data Heatmap with Spark Streaming Abstract […] Heatmap visualization is a well-known type of visual-ization to alleviate the overplot problem of point visualiza-tion. As such, it is well suited to visualize Big Data. In order to tackle the velocity problem of Big Data, one has to leverage […]
[Datasciencecentral] BigDataFr recommends: Representation of Numbers with Incredibly Fast Converging Fractions
BigDataFr recommends: Representation of Numbers with Incredibly Fast Converging Fractions […] Here we discuss a new system to represent numbers, for instance constants such as Pi, e, or log 2, using rational fractions. Each iteration doubles the precision (the number of correct decimals computed) making it converging much faster than current systems such as continued […]
[arXiv] BigDataFr recommends: Amplifying Inter-message Distance: On Information Divergence Measures in Big Data
BigDataFr recommends: Amplifying Inter-message Distance: On Information Divergence Measures in Big Data […] Subjects: Information Theory (cs.IT) Message identification (M-I) divergence is an important measure of the information distance between probability distributions, similar to Kullback-Leibler (K-L) and Renyi divergence. In fact, M-I divergence with a variable parameter can make an effect on characterization of distinction […]
[HAL] BigDataFr recommends: Random Forests for Big Data
BigDataFr recommends: Random Forests for Big Data Abstract […] Big Data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints. Big Data always involve massive data but they also often include online data and data heterogeneity. Recently some statistical methods have been adapted to process Big […]
[Dataconomy] BigDataFr recommends: Securing Competitive Advantage with Machine Learning
BigDataFr recommends: Securing Competitive Advantage with Machine Learning […] Business dynamics are evolving with every passing second. There is no doubt that the competition in today’s business world is much more intense than it was a decade ago. Companies are fighting to hold on to any advantages. Digitalization and the introduction of machine learning into […]
[Forbes France] BigDataFr recommande : Pourquoi Le Commerce “Physique” Pourrait Reprendre La Main
BigDataFr recommande : Pourquoi Le Commerce “Physique” Pourrait Reprendre La Main […] La révolution du web a clairement entraîné de profonds changements chez les acheteurs et de facto chez ceux qui sont donc devenus des e-commerçants. Toutefois, suite à la frénésie du tout digital des premières années, la donne est en train de changer vers […]
[Dataconomy] BigDataFr recommends: AI – The Present in the Making
BigDataFr recommends: AI – The Present in the Making […] For many people, the concept of Artificial Intelligence (AI) is a thing of the future. It is the technology that is yet to be introduced. But Professor Jon Oberlander disagrees. He was quick to point out that AI is not in the future, it is […]
[HAL] BigDataFr recommends: Eley: On the Effectiveness of Burst Buffers for Big Data Processing in HPC systems
BigDataFr recommends: Eley: On the Effectiveness of Burst Buffers for Big Data Processing in HPC systems Abstract […] Burst Buffer is an effective solution for reducing the data transfer time and the I/O interference in HPC systems. Extending Burst Buffers (BBs) to handle Big Data applications is challenging because BBs must account for the large […]
[arXiv] BigDataFr recommends: Visualization of Big Spatial Data using Coresets for Kernel Density Estimates
BigDataFr recommends: Visualization of Big Spatial Data using Coresets for Kernel Density Estimates […] Subjects: Human-Computer Interaction (cs.HC); Computational Geometry (cs.CG) The size of large, geo-located datasets has reached scales where visualization of all data points is inefficient. Random sampling is a method to reduce the size of a dataset, yet it can introduce unwanted […]