BigDataFr recommends : Computing on Masked Data to improve the Security of Big Data Organizations that make use of large quantities of information require the ability to store and process data from central locations so that the product can be shared or distributed across a heterogeneous group of users. However, recent events underscore the need […]
Author: Big Data
[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] BigDataFr recommends: Efficient Machine Learning for Big Data: A Review #datascientist #machine learning
BigDataFr recommends: Efficient Machine Learning for Big Data: A Review « With the emerging technologies and all associated devices, it is predicted that massive amount of data will be created in the next few years, in fact, as much as 90% of current data were created in the last couple of years,a trend that will continue […]
[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 […]
[ArXiv] BigDataFr recommends: Understanding Big Data Analytic Workload On Modern Processors #datascientist #machine learning
BigDataFr recommends: Understanding Big Data Analytic Workload On Modern Processors « Big data analytics applications play a significant role in data centers, and hence it has become increasingly important to understand their behaviors in order to further improve the performance of data center computer systems, in which characterizing representative workloads is a key practical problem. In […]
[arXiv] BigDataFr recommends StratOS – A Big Data Framework for Scientific Computing
BigDataFr recommends StratOS – A Big Data Framework for Scientific Computing Abstract ‘We introduce StratOS, a Big Data platform for general computing that allows a datacenter to be treated as a single computer. With StratOS, the process of writing a massively parallel program for a datacenter is no more complicated than writing a Python script […]