Macs in Chemistry

Insanely Great Science

KNIME update

 

What’s New in KNIME Analytics Platform 3.6.

  • KNIME Deep Learning
  • Constant Value Column Filter
  • Numeric Outliers
  • Column Expressions
  • Scorer (JavaScript)
  • Git Nodes
  • Call Workflow (Table Based)
  • KNIME Server Connection
  • Text Processing
  • Usability Improvements
  • Connect/Unconnect nodes using keyboard shortcuts
  • Zooming
  • Replacing and connecting nodes with node drop
  • Node repository search
  • Usability improvements in the KNIME Explorer
  • Copy from/Paste to JavaScript Table view/editor
  • Miscellaneous
  • Performance: Column Store (Preview)
  • Making views beautiful: CSS changes
  • KNIME Big Data Extensions
  • Create Local Big Data Environment
  • KNIME H2O Sparkling Water Integration
  • Support for Apache Spark v2.3
  • Big Data File Handling Nodes (Parquet/ORC)
  • Spark PCA
  • Spark Pivot
  • Frequent Item Sets and Association Rules
  • Previews
  • Create Spark Context via Livy
  • Database Integration
  • Apache Kafka Integration
  • KNIME Server

  • Management (Client Preferences)

  • Job View (Preview)
  • Distributed Executors (Preview)
  • General release notes

  • JSON Path library update

  • Java Snippet Bundle Imports

I suspect it will be the KNIME Deep learning that will catch the eye, the ability to set up deep learning models using drag and drop. Use regular Tensorflow models within KNIME Analytics Platform and seamlessly convert from Keras to Tensorflow for efficient network execution

deeplearning

The new Create Local Big Data Environment node creates a fully functional local big data environment including Apache Spark, Apache Hive and HDFS. It allows you to try out the nodes of the KNIME Big Data Extensions without a Hadoop cluster.


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KNIME Updated

 

KNIME Analytics Platform 3.4 is out

  • New Date & Time integration
  • Integration with the H2O machine learning library
  • KNIME Personal Productivity now part of KNIME Analytics Platform
  • Wrapped metanode composite view
  • A new version of the Python integration
  • Logistic Regression nodes are more scalable, faster, and support regularization
  • Audio and speech recognition nodes
  • JavaScript Views
  • New Cloud Connectors

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Dotmatics Reaction Workflows

 

Workflow tools have become increasingly popular Pipeline Pilot, Knime and Taverna and perhaps the best known. Most are desktop client based but some have a web page that allow users to run protocols that expert users have created.

Dotmatics Reaction Workflows (RW) is a web-based tool that allow users to build workflows from nodes that provide inputs and outputs or perform actions, including ones to perform reaction-, scaffold-, and transform-based enumeration, and it is all done within a web browser interface using drag and drop. I've been looking at reaction workflow for enumerating a potential library array.

Read more here….

rwdemofinal


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