Tag: Graph

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. Innovation in data processing and machine learning technology Putting a Groovy twist on Cloud Vision Monday, June 18, 2018 By Guillaume LaForge, Cloud Developer Advocate and Apache Groovy PMC Chair Powerful machine learning APIs are at your fingertips if you’re developing with Google Cloud Platform, as client libraries are available for various programming languages. Today, we’re investigating the Cloud Vision API and its Java SDK, using the Apache Groovy programming language—a multi-faceted language for the Java platform that aims to improve ... Read More

Feed: Neo4j Graph Database Platform. Author: Jocelyn Hoppa. By Jeff Morris, Product Team | June 18, 2018  From LinkedIn to Facebook to Google, top companies are driving their businesses using graphs. And use cases span industries as well.What those new to graph technology may not understand is that creating a graph does not require starting from scratch. Existing data stores, such as data lakes and relational databases, represent an onramp to graph analytics on your existing data.In this series, we’ll examine how investments in connected data return dividends for your bottom line – and beyond. In previous weeks, we’ve explored ... Read More

Feed: Planet MySQL; Author: Marcelo Altmann; Continuing on the series of blog posts about ProxySQL. Today I will demonstrate how to put ProxySQL in front of your master and slave topology and distribute reads and writes between the servers, without the need of changing your application code. To install ProxySQL and some of its basic concepts, please read this blog post. On this tutorail, we will use 4 servers: ProxySQL (192.168.112.60) Node1 – Master (192.168.112.61) Node2 – Slave-1 (192.168.112.62) Node3 – Slave-3 (192.168.112.63) Servers With ProxySQL already installed, we will connect into the admin interface, add our 3 servers and configure Replication ... Read More

Feed: Neo4j Graph Database Platform. Author: Mark Needham. Welcome to this week in Neo4j where we round up what’s been happening in the world of graph databases in the last 7 days. This week we have a tutorial of the Neo4j ETL Tool, a React.js application that uses the new temporal and geospatial data types, a new release of the JDBC Driver, and more! Have a really awesome Neo4j story or use case you want to share with the world? Sign up for our Neo4j Speaker and/or Neo4j Blogger program! Learn More This week’s featured community member is David Meza, ... Read More

Feed: R-bloggers. Author: Roos Colman. In this post, I will illustrate the use of prediction intervals for the comparison of measurement methods. In the example, a new spectral method for measuring whole blood hemoglobin is compared with a reference method. But first, let's start with discussing the large difference between a confidence interval and a prediction interval. Prediction interval versus Confidence interval Very often a confidence interval is misinterpreted as a prediction interval, leading to unrealistic “precise” predictions. As you will see, prediction intervals (PI) resemble confidence intervals (CI), but the width of the PI is by definition larger than ... Read More

Feed: Planet MySQL; Author: Dimitri Kravtchuk; Generally, when I'm analyzing MySQL Performance on Linux with "localhost" test workloads, I'm configuring client connections to use IP port (loopback) to connect to MySQL Server (and not UNIX socket) -- this is still at least involving IP stack in the game, and if something is going odd on IP, we can be aware ahead about. And indeed, it already helped several times to discover such kind of problems even without network links between client/server (like this one, etc.). However, in the past we also observed a pretty significant difference in QPS results when ... Read More

Feed: All - O'Reilly Media. Author: Nicole Forsgren. This is a keynote highlight from the O'Reilly Velocity Conference in San Jose 2018. Watch the full version of this keynote on O'Reilly's online learning platform. You can also see other highlights from the event. Article image: Performance graph (source: Pixabay). Nicole Forsgren is the CEO and chief scientist at DevOps Research and Assessment (DORA). Nicole is an IT impacts expert who is best known for her work with tech professionals and as the lead investigator on the largest DevOps studies to date. She is a consultant, expert, and researcher in knowledge ... Read More

Feed: Blog Post – Corporate – DataStax. Author: Mark Simborg, Editorial Director. The eCommerce market is thriving. The global eCommerce market topped 2.4 trillion in 2017 and is expected to nearly double to $4.058 trillion by 2020. Getting your slice of that ever-increasing pie starts with creating powerful eCommerce experiences that keep your customers coming back for more. Doing that requires choosing the right database to power your customer-facing eCommerce applications. As you begin your search for the best database for eCommerce applications, you’ll quickly find out you have a lot of options. What features should your eCommerce database include? ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog. Author: Google Cloud Big Data and Machine Learning Blog Team. Innovation in data processing and machine learning technology Introducing Cloud Dataflow’s new Streaming Engine Thursday, June 14, 2018 By Sergei Sokolenko, Cloud Dataflow Product Manager Many of our customers benefit from the design principle of separating compute from state storage used by Google in several Big Data services such as BigQuery and, most recently, in batch Cloud Dataflow pipelines using the Dataflow Shuffle. Today, we are launching Cloud Dataflow Streaming Engine in beta, to apply the same principle to streaming pipelines ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Sanjiban Sekhar Roy. BOOK : Big Data in Engineering Applications, SPRINGER. Editors: Roy, S.S., Samui, P., Deo, R., Ntalampiras, S. (Eds.). Dear All, The most anticipated book titled "Big Data in Engineering Applications" has been published by Springer. This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. The book is a ... Read More