Tag: Big Data

Feed: R-bloggers. Author: John Mount. We are thrilled to announce our (my and Nina Zumel’s) paper on the dot-pipe has been accepted by the R-Journal! A huge “thank you” to the reviewers and editors for helping us with this! You can find our article here (pdf here)! Related To leave a comment for the author, please follow the link and comment on their blog: R – Win-Vector Blog. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, ... Read More

Feed: R-bloggers. Author: rOpenSci - open tools for open science. Sharing data sets for collaboration or publication has always been challenging, but it’s become increasingly problematic as complex and high dimensional data sets have become ubiquitous in the life sciences. Studies are large and time consuming; data collection takes time, data analysis is a moving target, as is the software used to carry it out. In the vaccine space (where I work) we analyze collections of high-dimensional immunological data sets from a variety of different technologies (RNA sequencing, cytometry, multiplexed antibody binding, and others). These data often arise from clinical ... Read More

Feed: IBM Big Data & Analytics Hub - All Content; Author: jessica-lee; Data application resiliency is high on the agenda for CIOs. Gary Brunell from IBM looks at the in-house solution developed by Hotels.com, and how the IBM Big Replicate offering compares. Gary Brunell is a Certified Thought Leader, and a Distinguished IT Specialist at IBM. Gary works with a wide range of clients in the financial services sector, from startups to Fortune 50 companies. Jessica Lee: Hotels.com has developed an intrepid approach to data consistency data management challenges. What’s the story behind its data strategy? Gary Brunell: Hotels.com, an ... Read More

Feed: IBM Big Data & Analytics Hub - All Content; Author: hemant-suri; Today’s digital enterprise needs fast and actionable insights. This is an increasingly tall order because data resides in higher volumes, greater variety and more locations throughout the organization, and a rising number of business users demand that data. Coupled with the choice and complexity of deployments (on premises, public and multicloud), this makes data management and integration extremely challenging. To succeed, enterprises should adopt a hybrid data approach to all things data and analytics. The IBM Hybrid Data Management strategy is laser-focused on helping clients unlock their most valuable ... Read More

Feed: Planet big data. Author: David Smith. I had a great time at the EARL Conference in London last week, and as always came away invigorated by all of the applications of R that were presented there. I'll do a full writeup of the conference later this week, but in the meantime I wanted to share the materials from my own presentation there, "Not Hotdog: Image Recognition with R and the Custom Vision API". I've embedded the slides below: In the presentation, I showed how easy it is to build a Shiny app to perform image recognition with the Custom ... Read More

Feed: R-bloggers. Author: Ivan Svetunkov. Intro One of the features of functions of smooth package is the ability to work with intermittent data and the data with periodically occurring zeroes. Intermittent time series is a series that has non-zero values occurring at irregular frequency (Svetuknov and Boylan, 2017). Imagine retailer who sells green leap sticks. The demand on such a product will not be easy to predict, because green colour is not a popular colour in this case, and thus the data of sales will contain a lot of zeroes with seldom non-zero values. Such demand is called “intermittent”. In ... Read More

Feed: R-bloggers. Author: business-science.io - Articles. Artificial intelligence (AI) has the potential to change industries across the board, yet few organizations are able to capture its value and realize a real return-on-investment. The reality is that the transition to AI and data driven analysis is difficult and not well understood. The issue is twofold, first, the necessary technology to complete such a task has only recently become mainstream, and second, most data scientists are inexperienced in their respective industries. However, with all the uncertainty surrounding this topic, one hedge fund has managed to navigate through these challenges and accomplish what ... Read More

Feed: Planet big data. Author: Praveen Sripati. In K8S, everything is declarative and not imperative. We specify the target state to K8S and it will make sure that that the target state is always there, even in the case of failures. Basically, we specify what we want (as in the case of SQL) and not how to do it. In the above scenario, we have 1 master and 2 nodes. We can ask K8S to deploy 6 pods (application instances) onto the nodes and K8S will automatically schedule the pods across the nodes. In case one of the node goes ... Read More

Feed: Planet MySQL; Author: Baron Schwartz (xaprb); For various reasons, I’ve become interested in analytical databases. These are traditionally called OLAP (online analytics processing) and are designed to extract insights from very large datasets, often with expectations of long response times (hours). More recently, though, various databases capable of running relatively interactive queries over large datasets have emerged. This post is more-or-less a list of analytic databases, with somewhat of a taxonomy added. As with any list of this type, categories are inexact, and I’m sure this is only partial. I’m making some value judgments about what to mention and ... Read More

Feed: R-bloggers. Author: statcompute. In the post (https://statcompute.wordpress.com/2018/09/15/how-to-avoid-for-loop-in-r), I briefly introduced the idea of vectorization and potential use cases. One might be wondering why we even need the Vectorize() function given the fact that it is just a wrapper and whether there is any material efficiency gain by vectorizing a function. It is true that the Vectorize() function is not able to improve the efficiency of any function itself that is wrapped around, e.g. vectorized. However, the vectorization can change the input format of a function that normally consumes scalar inputs before being vectorized and therefore would improve the processing ... Read More