Hub
  • Software
  • Blog
  • Forum
  • Events
  • Documentation
  • About KNIME
  • KNIME Hub
  • Nodes
  • Workflow Combiner
NodeNode / Manipulator

Workflow Combiner

Workflow Abstraction Integrated Deployment
Drag & drop
Like
Copy short link

Allows to connect various workflows into one workflow. Free output ports from one workflow are connected to the free input ports of the consecutive workflow. The actual pairing of those output and input ports can be configured.
Please note that the workflow name as well as the workflow editor settings (such as grid or connection settings) of the result workflow will be inherited from the workflow at the first input port.

Node details

Input ports
  1. Type: Workflow Port Object
    First workflow
    First workflow to be connected.
  2. Type: Workflow Port Object
    Second workflow
    Second workflow to be connected.
Output ports
  1. Type: Workflow Port Object
    Connected workflow
    Workflow derived by connecting all input workflows in order of their appearance.
workflow model (Dynamic Inport)
Workflow to be connected to its predecessor.
  1. Type: Workflow Port Object

Extension

The Workflow Combiner node is part of this extension:

  1. Go to item

Related workflows & nodes

  1. Go to item
    Integrated Deployment - Solution
    Export Write Pmml
    +3
    Solution to the exercise 6.1 for KNIME User Training - Train a Decision Tree model - Capt…
    knime > Education > Courses > L2-DS KNIME Analytics Platform for Data Scientists - Advanced > solutions > 10.1 Integrated Deployment - solution
  2. Go to item
    Integrated Deployment - Solution
    Export Write Pmml
    +3
    Solution to the exercise 6.1 for KNIME User Training - Train a Decision Tree model - Capt…
    tenalf > Public > L2-DS KNIME Analytics Platform for Data Scientists - Advanced > solutions > 10.1 Integrated Deployment - solution
  3. Go to item
    Integrated Deployment Solution
    Machine learning Education Life science
    +1
    Solution to the "Integrated Deployment" exercise for the advanced Life Science User Train…
    rubiocarolina > Public > Solutions > 04.1. Integrated Deployment - solution
  4. Go to item
    Integrated Deployment Solution
    Machine learning Education Life science
    +1
    Solution to the "Integrated Deployment" exercise for the advanced Life Science User Train…
    knime > Education > Courses > L2-LS KNIME Analytics Platform for Data Scientists - Life Sciences - Advanced > Solutions > 04.1. Integrated Deployment - solution
  5. Go to item
    Integrated Deployment Solution
    Machine learning Education Life science
    +1
    Solution to the "Integrated Deployment" exercise for the advanced Life Science User Train…
    catherineoleary > Public > L2-LS KNIME Analytics Platform for Data Scientists - Life Sciences - Advanced > Solutions > 04.1. Integrated Deployment - solution
  6. Go to item
    COPA 2022 - Redfield & JU - Integrated deployment
    tuwelofstrom > Public > COPA 2022 - Conformal Prediction in KNIME > COPA 2022 - Redfield & JU - Integrated deployment
  7. Go to item
    Integrated Deployment Example
    Integrated Deployment Microsoft Authentication SharePoint
    +1
    This workflow shows the Integrated Deployment Extension nodes and how to combine them to …
    knime > Examples > 09_Enterprise > 04_Integrated_Deployment > 04_Integrated_Deployment_Extension_Guide > 01_Integrated_Deployment_Example
  8. Go to item
    Day5 Analytics - Cost Prediction with PyCaret
    Machine learning PyCaret Python
    +2
    Demonstrates the building of a deployable machine learning model using KNIME Integrated D…
    supersharp > Finance, Accounting and Audit Automation > Cost Prediction with PyCaret > Day5 Analytics - Cost Prediction with PyCaret
  9. Go to item
    Taxi demand prediction training workflow
    Demand prediction Random forest Time series prediction
    +5
    There has been no description set for this workflow's metadata.
    knime > Codeless Time Series Analysis with KNIME > Chapter 13 > StockPrediction_Training
  10. Go to item
    Keras Autoencoder for Fraud Detection - Integrated Deployment
    Autoencoder Keras Neural network
    +13
    This workflow replicates the exercises of session 2, combining training and deployment us…
    knime > Education > Courses > L4-DL Introduction to Deep Learning > Supplementary workflows > Autoencoder > 01_Fraud_Detection_Integrated_Deployment

No known nodes available

KNIME
Open for Innovation

KNIME AG
Hardturmstrasse 66
8005 Zurich, Switzerland
  • Software
  • Getting started
  • Documentation
  • E-Learning course
  • Solutions
  • KNIME Hub
  • KNIME Forum
  • Blog
  • Events
  • Partner
  • Developers
  • KNIME Home
  • KNIME Open Source Story
  • Careers
  • Contact us
Download KNIME Analytics Platform Read more on KNIME Server
© 2022 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Credits