High Charts is a JavaScript charting library designed for use in web applications, dashboards, grids and frameworks including React. It offers products for core charting, stock data, maps, Gantt charts, grids and dashboards, and is used by a large number of global enterprises. The library provides built-in adaptive functionality and extensive customization options for interactive data visualizations.
Apache Hadoop is an open-source project that develops software for reliable, scalable, distributed computing. Its library is a framework for distributed processing of large data sets across clusters of computers using simple programming models, scaling from single servers to thousands of machines with local computation and storage, and detecting failures at the application layer.
Apache Hama is a retired Apache Software Foundation Top-Level Project established in 2012. It is a framework for Big Data analytics that uses the Bulk Synchronous Parallel (BSP) computing model. It also provides vertex-centric and neuron-centric programming models inspired by Google's Pregel and DistBelief. Its components include core BSP, Apache MRQL for query processing, a graph computing package, and Apache Horn for deep learning, and it integrates with Hadoop HDFS.
MatlabBGL is a Matlab package for working with graphs that uses the Boost Graph Library to implement graph algorithms. It is designed for large sparse graphs with hundreds of thousands of nodes and was written by David Gleich. The page lists version 4.0 as the latest release, with downloads hosted on the Matlab File Exchange.
DataTalks.Club. A weekly newsletter about data-related things..
InnoCentive describes itself as a science, technology and innovation crowd connecting organizations with individual solvers. Companies and universities post challenges with awards, while solvers submit proposals; the site lists live challenges and a marketplace for funded opportunities.
The Data Science Lab is a blog of experiments with data. Posts cover topics including k-means clustering, k-means++ seeding, the gap statistic, and an alternative approach to selecting the optimal number of clusters based on a 2004 publication by Pham, Dimov and Nguyen. A 2014 annual report notes the blog was viewed about 74,000 times that year.
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Apache Hadoop is an open-source project that develops software for reliable, scalable, distributed computing. Its library is a framework for distributed processing of large data sets across clusters of computers using simple programming models, scaling from single servers to thousands of machines with local computation and storage, and detecting failures at the application layer.
Apache Hama is a retired Apache Software Foundation Top-Level Project established in 2012. It is a framework for Big Data analytics that uses the Bulk Synchronous Parallel (BSP) computing model. It also provides vertex-centric and neuron-centric programming models inspired by Google's Pregel and DistBelief. Its components include core BSP, Apache MRQL for query processing, a graph computing package, and Apache Horn for deep learning, and it integrates with Hadoop HDFS.
MatlabBGL is a Matlab package for working with graphs that uses the Boost Graph Library to implement graph algorithms. It is designed for large sparse graphs with hundreds of thousands of nodes and was written by David Gleich. The page lists version 4.0 as the latest release, with downloads hosted on the Matlab File Exchange.
DataTalks.Club. A weekly newsletter about data-related things..
InnoCentive describes itself as a science, technology and innovation crowd connecting organizations with individual solvers. Companies and universities post challenges with awards, while solvers submit proposals; the site lists live challenges and a marketplace for funded opportunities.
The Data Science Lab is a blog of experiments with data. Posts cover topics including k-means clustering, k-means++ seeding, the gap statistic, and an alternative approach to selecting the optimal number of clusters based on a 2004 publication by Pham, Dimov and Nguyen. A 2014 annual report notes the blog was viewed about 74,000 times that year.
