Exploring and Modifying KNIME Workflows

KNIME or Konstanz Information Miner is an end-to-end data analytics platform. It is used for building predictive models as well. It is a platform that is used to manipulate and analyze the data present. KNIME is based on node-interface here we define the workflow using nodes. It is free to use and can be integrated with many coding languages like R, Python and Java.

Understanding KNIME Analytics Components

KNIME workflows are visual representations of data processing tasks. They consist of nodes that perform specific operations on data, such as reading, transforming, and visualizing it. These nodes are connected by data paths that define the flow of data between them. The modular nature of KNIME allows users to build complex workflows by combining simple nodes.

There are some components of KNIME workflow. Let us have a look at each of them:

• Nodes: A node is the smallest unit in KNIME. Each node has some functionalities. For example: reading data, replacing missing values etc.
• Connection: A connection is a link that is established between two or more nodes. It allows data to flow from one node to another.
• Workspace: This is the blank space where we drop our nodes, establish connection and create an interactive workflow.

Opening Existing Workflows in KNIME

Now there are two types of User Interfaces available in KNIME. One is Classical and the other is Modern.

Modern UI

1. Click on Home

2. Click on Local space

Click on Local Space as all the workflows are saved in the local space by default.

3. Double click on any workflow

The workflow will get opened.

Classic User Interface

For Classic User Interface, we have to import the workflow. The steps are as follows:

1. Click on File

2. Then Click on Import Workflow

In the local system, the workflows are saved with .knwf extension.

3. Then Browse the location where the workflow has been saved.

4. Click on Finish

Adding and Connecting Nodes in KNIME Workflow

In KNIME, nodes are present in node repository. In Node Repository, there are multiple sections and in each section nodes are present.

Now click on any particular section and under the section, click on any node and drag it in the workspace. Now to connect the nodes drag and drop the nodes, then drag the link of the output of one node to another.

For instance, here we have used Excel Reader to read the excel file. Execute the node and connect the pointer of the Excel Reader to Column Filter. Then execute the node.

Modifying KNIME Workflows Settings

1. Providing With Configurations

To modify the workflow settings, we need to configure the nodes as per our requirements. Double click on any node or press F6. The dialog box will open. Adjust the parameters or provide the file paths and finally execute the node. For example in Excel Reader double click and provide with the file paths.

2. Adjusting Flow Variables

Flow variable is a dynamic entity that is used to control the node settings. These variables are used to make the workflows more automated as these variables are used to perform the repetitive tasks.

The Flow variables are accessible in the Configuration windows only.

Executing and Debugging Workflows

To execute a particular node, after configuring the settings:

• Click on the play button.
• The output will be displayed below.

For example here we are using CSV Reader to read the file. Click on Play button present at the top of the node or in the below click on Execute button.

Debugging Workflows

Errors can occur in one or more nodes. If the error is not resolved the workflow will stop at that particular node where the error has occurred. It will not continue with the rest of the execution. The error will appear when we hover over the node.

Consider a case where we are using Excel Reader node to read an Excel file and we have specified the path incorrectly. After executing the node, we can see a cross symbol appears on the yellow circle. Upon hovering we can see the 'File Not Found' error. In the KNIME console also we can see the details of the error.

To debug the error we have to open the Configuration settings and do the necessary changes as required.

Enhancing User Experience

Improving the user experience in KNIME involves customizing workflow components and interfaces:

• Workflow Application Header: Customize the header of your data apps using the Workflow Application Header component. This component allows you to set title sizes, colors, and icons, enhancing the visual appeal of your applications.
• Excel Formatting: Use components like "XLSX Simple Excel Format Apply" to apply formatting styles such as bold text or color settings when exporting data to Excel. This ensures that exported reports maintain a professional appearance.

Conclusion

Exploring and modifying KNIME workflows requires a combination of good practices in formatting, documentation, and customization. By organizing nodes into metanodes, adding annotations, documenting thoroughly, and utilizing components effectively, users can create efficient and comprehensible workflows.

These strategies not only improve individual productivity but also facilitate collaboration within teams using KNIME Analytics Platform.

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Article Tags :

• Data Analysis
• AI-ML-DS
• AI-ML-DS With Python