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 […]
Innovation
[SAS White Paper] BigDataFr recommends: A Non-Geek’s Big Data Playbook
BigDataFr recommends: SAS White Paper – A Non-Geek’s Big Data Playbook Hadoop and the Enterprise Data Warehouse ‘This paper examines how a non-geek yet technically savvy business professional can understand how to use Hadoop – and how it will impact enterprise data environments for years to come. The paper serves as a playbook that demonstrates […]
[ERIC – Institut of Education Sciences] BigDataFr recommends: Research on Implementing Big Data Technology, People, & Processes #datascientist #machinelearning
BigDataFr recommends: Research on Implementing Big Data: Technology, People, & Processes ‘Abstract: When many people hear the term “big data”, they primarily think of a technology tool for the collection and reporting of data of high variety, volume, and velocity. However, the complexity of big data is not only the technology, but the supporting processes, […]
[fivethirtyeight] BigDataFr recommends: How Data Nerds Found A 131-Year-Old Sunken Treasure
BigDataFr recommends: How Data Nerds Found A 131-Year-Old Sunken Treasure ‘The SS Central America, a steamer carrying a cache of gold, sank off the southeast coast of the United States in 1857. Part mystery, part adventure story, “In Deep Water,” directed by Steven Leckart and presented by ESPN Films and FiveThirtyEight, recounts the tale of […]
[O’R] BigDataFr recommends: Data science makes an impact on Wall Street #datascientist #machinelearning #quant
BigDataFr recommends: Data science makes an impact on Wall Street The O’Reilly Data Show Podcast: Gary Kazantsev on how big data and data science are making a difference in finance. ‘Having started my career in industry, working on problems in finance, I’ve always appreciated how challenging it is to build consistently profitable systems in this […]
[arxiv] BigDataFr recommends: Computing on Masked Data to improve the Security of Big Data
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 […]
[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 […]
[Futura-Sciences] BigDataFr recommande : Le superordinateur Watson s’attaquera-t-il au cancer ?
BigDataFr recommande: Le superordinateur Watson s’attaquera-t-il au cancer ? « Des burritos bœuf-chocolat, un dessert au bacon et aux champignons : ce sont quelques recettes de Chef Watson proposées à la dégustation lors d’une conférence organisée cette semaine à New York par le groupe informatique américain pour vanter les possibilités de son superordinateur et convaincre des […]
[IBM] BigDataFr recommends: The power behind Apache Spark #machinelearning #datascientist #hadoop #ibm
BigDataFr recommends: The power behind Apache Spark « Building robust analytics applications requires careful planning, many iterations and fine tuning. Imagine reusing and dynamically updating a basic set of models more quickly than ever to address a broad set of requirements with a simple tag. For example, when developing a watch list of terms to monitor […]