BigDataFr recommends: Data Integration Tools – Market Study […] This post is a brief review of leading Data Integration tools in the market. Heavily referencing from the Gartner 2016 report and peer reviews from my circle The Market The data integration tool market was worth approximately $2.8 billion at the end of 2015, an increase […]
Month: octobre 2016
[arXiv] BigDataFr recommends: Big Data Analytics in Cloud environment using #Hadoop
BigDataFr recommends: Big Data Analytics in Cloud environment using Hadoop Subjects: Distributed, Parallel, and Cluster Computing (cs.DC) […] The Big Data management is a problem right now. The Big Data growth is very high. It is very difficult to manage due to various characteristics. This manuscript focuses on Big Data analytics in cloud environment using […]
[arXiv] BigDataFr recommends: Correct classification for big/smart/fast data machine learning
BigDataFr recommends: Correct classification for big/smart/fast data machine learning Subjects: Learning (cs.LG); Information Theory (cs.IT) […] Table (database) / Relational database Classification for big/smart/fast data machine learning is one of the most important tasks of predictive analytics and extracting valuable information from data. It is core applied technique for what now understood under data science […]
[Datasciencecentral] BigDataFr recommends: Beyond Deep Learning – 3rd Generation Neural Nets
BigDataFr recommends: Beyond Deep Learning – 3rd Generation Neural Nets […] “By far the fastest expanding frontier of data science is AI and specifically the rapid advances in Deep Learning. Advances in Deep Learning have been dependent on artificial neural nets and especially Convolutional Neural Nets (CNNs). In fact our use of the word “deep” […]
[arXiv] BigDataFr recommends: Measuring Economic Resilience to Natural Disasters with Big Economic Transaction Data
BigDataFr recommends: Measuring Economic Resilience to Natural Disasters with Big Economic Transaction Data Subjects: Databases (cs.DB) […] This research explores the potential to analyze bank card payments and ATM cash withdrawals in order to map and quantify how people are impacted by and recover from natural disasters. Our approach defines a disaster-affected community’s economic recovery time […]
[arXiv] BigDataFr recommends : Big Data analytics. Three use cases with R, Python and #Spark #datascientist
BigDataFr recommends: Big Data analytics. Three use cases with R, Python and Spark Subjects: Applications (stat.AP); Learning (cs.LG) […] Management and analysis of big data are systematically associated with a data distributed architecture in the Hadoop and now Spark frameworks. This article offers an introduction for statisticians to these technologies by comparing the performance obtained […]
[Datasciencecentral] BigDataFr recommends: How Data Is Changing the Future of Smart Health Technology
BigDataFr recommends: How Data Is Changing the Future of Smart Health Technology […] “Many products and applications exist today that can help us take steps toward healthier living. » – Ben Bajarin Big data is everywhere. Yes it’s true that it has always been there but, we are just recently finding ways to best utilize it. […]