This workflow aims to demonstrate the use of the Expressions node for data manipulation, including string and integer operations, and to highlight the power of combining AI techniques.

You can download and run the workflow directly in your KNIME Analytics Platform. We recommend using the latest KNIME AP version for optimal performance.

We are using a fake dataset to show e-commerce performance metrics.

Workflow Details:

Below is how we have used the Expression node within the workflow.

Age Bucket Creation
- First, we create an age bucket using an IF condition, following the example provided by the node IF expression.
Expression Node and K-AI Assistant
- We use the Expressions node with K-AI (KNIME Assistant) to generate regex patterns for extracting data. Remember to install the AI assistant extension to use this feature. We provide the assistant with two different prompts to achieve the desired results:
- Prompt Product ID:
  "Generate a regular expression to extract the Product ID from the URL column.
  Example:
  - URL: https://www.fakeecommerce.com/electronics/fire/9b39b2b4-b6a6-418e-a994-8b1c0aad8a54?source=Google
  - Product ID pattern: b39b2b4-b6a6-418e-a994-8b1c0aad8a54"
- Prompt Product Category:
  "Generate a regular expression to extract the Product category from the URL column.
  The product category is always the first element of the URL pathname."
Data Transformation
- Perform a join operation and apply title case formatting to the Product Category and Age Bucket columns.
Revenue Calculations
- Perform mathematical operations to calculate the percentage of revenues.

Finally, with the Table View node, it is possible to visualize the final data.

	Go back to parent directory
	01_Handling_Missing_Values
	02_StringManipulation_MathFormula_RuleEngine
	03_Column_Expressions_for_Data_Manipulation
	04_StringManipulation_MultiColumn
	05_TableDifference_Finder
	06_Multi-Row_Formula_using_Column_Expression
	07_File_Path_Expressions
	Anonymize PII in contract data
	Data Manipulation with Expressions and AI assistant
	Dataset cleaning and formatting
	String Cleaner Example

Examples