[arXiv] BigDataFr recommends: Identifying Dwarfs Workloads in Big Data Analytics #datascientist #machinelearning

BigDataFr recommends: Identifying Dwarfs Workloads in Big Data Analytics ‘Big data benchmarking is particularly important and provides applicable yardsticks for evaluating booming big data systems. However, wide coverage and great complexity of big data computing impose big challenges on big data benchmarking. How can we construct a benchmark suite using a minimum set of units […]

[arXiv] BigDataFr recommends: Online Updating of Statistical Inference in the Big Data Setting

BigDataFr recommends: Online Updating of Statistical Inference in the Big Data Setting ‘We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models […]

[arXiv] BigDataFr recommends: Network Filtering for Big Data

BigDataFr recommends: Network Filtering for Big Data: Triangulated Maximally Filtered Graph ‘We propose a network-filtering method, the Triangulated Maximally Filtered Graph (TMFG), that provides an approximate solution to the Weighted Maximal Planar Graph problem. The underlying idea of TMFG consists in building a triangulation that maximizes a score function associated with the amount of information […]

[arXiv] BigDataFr recommends: Two models at work on Big Data application frameworks

BigDataFr recommends: Actors vs Shared Memory: two models at work on Big Data application frameworks ‘This work aims at analyzing how two different concurrency models, namely the shared memory model and the actor model, can influence the development of applications that manage huge masses of data, distinctive of Big Data applications. The paper compares the […]

[arXiv] Big Data Analytics for Dynamic Energy Management in Smart Grids #datascientist #machinelearning

BigDataFr recommends: Big Data Analytics for Dynamic Energy Management in Smart Grids ‘The smart electricity grid enables a two-way flow of power and data between suppliers and consumers in order to facilitate the power flow optimization in terms of economic efficiency, reliability and sustainability. This infrastructure permits the consumers and the micro-energy producers to take […]

[arXiv] BigDataFr recommends: On the Feasibility of Distributed Kernel Regression for Big Data #datascientist #machinelearning

BigDataFr recommends: On the Feasibility of Distributed Kernel Regression for Big Data « In modern scientific research, massive datasets with huge numbers of observations are frequently encountered. To facilitate the computational process, a divide-and-conquer scheme is often used for the analysis of big data. In such a strategy, a full dataset is first split into several […]

[arXiv] BigDataFr highly recommends: Leading Undergraduate Students to Big Data Generation #datascientist #machinelearning #conceptlearning

BigDataFr highly recommends: Leading Undergraduate Students to Big Data Generation Introduction « People are facing a flood of data today. Data are being collected at unprecedented scale in many areas, such as networking[14][2][4], image processing[15 ][5], visualization[12], scientific computation, data base[17][18], and algorithms. The huge data nowadays are called Big Data. Big data is an all-encompassing […]

[arXiv] #datascientist BigDataFr recommends: Overview of Some New Digital Technology Trends Big Data

BigDataFr recommends: Overview of Some New Digital Technology Trends Big Data (Societal, Economic, Ethical and Legal Challenges of the Digital Revolution: From Big Data to Deep Learning, Artificial Intelligence, and Manipulative Technologies) « In a globalized world, companies and countries are exposed to a harsh competition. This produces a considerable pressure to create more efficient systems […]