Tag: metadata

Feed: MariaDB Knowledge Base Article Feed. Author: . This upgrade also applies to 1.2.0 Alpha to 1.2.3 GA upgrades Changes in 1.2.1 and later Non-distributed is the default distribution mode in postConfigure The default distribution mode has changed from 'distributed' to 'non-distributed'. During an upgrade, however, the default is to use the distribution mode used in the original installation. The options '-d' and '-n' can always be used to override the default. Non-root user sudo setup Root-level permissions are no longer required to install or upgrade ColumnStore for some types of installations. Installations requiring some level of sudo access, and ... Read More

Feed: Planet big data. Author: Praveen Sripati. In this blog previously we explored the different ways of getting started with K8S using Play-With-Kubernetes, Minikube and finally easily installing K8S using a combination of VirtualBox and Vagrant.Now we will try to install Istio on top of the existing K8S setup and install a Microservices based application called Bookinfo on top of it. Once the setup of Istio is done, we should be able to explore the different features of Istio. The Bookinfo Application is a polygot application using Python, Java, Ruby and NodeJS. The various Microservices and how there interact is ... Read More

Feed: All - O'Reilly Media. Author: Patrick Hall. Cyber crime (source: Pixabay) Check out the "Ethics, Privacy, and Security" sessions at the Artificial Intelligence Conference in New York, April 15-18, 2019. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. A recent flourish of posts and papers has outlined the broader topic, listed attack vectors and vulnerabilities, started to propose defensive solutions, and provided the necessary framework for this post. The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such ... Read More

Feed: R-bloggers. Author: John Mount. From https://twitter.com/sharon000/status/1107771331012108288: From https://tidyr.tidyverse.org/dev/articles/pivot.html: There are two important new features inspired by other R packages that have been advancing of reshaping in R: The reshaping operation can be specified with a data frame that describes precisely how metadata stored in column names becomes data variables (and vice versa). This is inspired by the cdata package by John Mount and Nina Zumel. For simple uses of pivot_long() and pivot_wide(), this specification is implicit, but for more complex cases it is useful to make it explicit, and operate on the specification data frame using dplyr and tidyr ... Read More

Feed: R-bloggers. Author: hrbrmstr. There’s been alot of talk about “dependencies” in the R universe of late. This is not really a post about that but more of a “really, don’t do this” if you decide you want to poke the dependency bear by trying to build a deeply flawed model off of CRAN package metadata. CRAN packages undergo checks. Here’s one for akima (I :heart: me some gridded interpolation functions, plus this package is not in any hot-button R tribe right now): Flavor Version Tinstall Tcheck Ttotal Status Flags r-devel-linux-x86_64-debian-clang 0.6-2 9.83 32.67 42.50 OK r-devel-linux-x86_64-debian-gcc 0.6-2 8.53 26.56 ... Read More

Feed: R-bloggers. Author: Learning Machines. As promised in the post Learning Data Science: Modelling Basics we will now go a step further and try to predict income brackets with real world data and different modelling approaches. We will learn a thing or two along the way, e.g. about the so-called Accuracy-Interpretability Trade-Off, so read on… The data we will use is from here: Marketing data set. The description reads: This dataset contains questions from questionnaires that were filled out by shopping mall customers in the San Francisco Bay area. The goal is to predict the Annual Income of Household from ... Read More

Feed: What's New. Amazon Rekognition provides a comprehensive set of face detection, analysis, and recognition features for image and video analysis. Today, we are launching accuracy enhancements to the face analysis features. This is the fifth model update overall since the service launched. Face analysis generates rich metadata about detected faces in the form of gender, age range, emotions, attributes such as ‘Smile’, ‘Eyeglasses’ and ‘Beard’, face pose, face image quality and face landmarks. With this release, we have improved the accuracy of gender identification, emotion detection (for all 7 supported emotions: ‘Happy’, ‘Sad’, ‘Angry’, ‘Surprised’, ‘Disgusted’, ‘Calm’ and ‘Confused’) ... Read More

Feed: Cloud Data Transformation Software | Matillion. Author: Paul Johnson, Solution Architect; Paul Johnson, Solution ArchitectOur customers have special data storage requirements, especially around volume and variety (unstructured data). To meet their needs, they are employing data lake architectures.A data lake can store data in any volumes and make it available without needing to transform it before being ready for analysis. This ability can address unique requirements by providing a cost effective resource for scaling, storing and accessing large volumes of diverse data types. Data lakes, however, do not come without their challenges. In this blog, we outline common challenges ... Read More

Feed: Microsoft Azure Blog. Author: Sunil Kamath. We’re excited to announce a partnership with Timescale that introduces support for TimescaleDB on Azure Database for PostgreSQL for customers building IoT and time-series workloads. TimescaleDB has a proven track record of being deployed in production in a variety of industries including oil & gas, financial services, and manufacturing. The partnership reinforces our commitment to supporting the open-source community to provide our users with the most innovative technologies PostgreSQL has to offer. TimescaleDB allows you to scale for fast ingest and complex queries while natively supporting full SQL. It leverages PostgreSQL as an ... Read More

Feed: Blog – Hazelcast. Author: Ensar Basri Kahveci. The CP Subsystem of Hazelcast IDMG 3.12 offers a new linearizable implementation of Hazelcast’s concurrency APIs on top of the Raft consensus algorithm. These implementations live alongside AP data structures in the same Hazelcast IMDG cluster (new BFFs, yay!). You can store large data sets on hundreds of Hazelcast members and coordinate your operations using the new safe concurrency primitives that reside in the very same cluster. This design poses a significant challenge. Hazelcast IMDG is known for its dynamic and elastic clustering capabilities. You can separately configure backups for your data ... Read More