Getting Started Guide

To get started, first download the CSV-file that contains the data that you are going to use in the workflow. Open your Analytics Platform and create a new, empty workflow by clicking “New” in the toolbar.

From the download folder, drag and drop the CSV-file into the workbench editor. A File Reader node will appear on the workflow editor and its configuration dialog will pop-up.

Here you can see the path to the file you dropped into the workflow editor and a preview of the data table. Click OK and execute the File Reader node by right-clicking the node and selecting Execute from the context menu. Now the input data are available at the output port of the File Reader node. To view the output table, right-click the executed node and select the last option in the menu: File Table.

To filter some of the columns out, use the Column Filter node.

In the node repository panel on the left:

• write “column filter node” in the search field
• drag&drop the Column Filter node to your workflow editor
• connect its input port with the output port of the File Reader node

To open the configuration dialog, right-click the node and choose Configure.

Here, move the “columns”, “country”, “date”, and “amount” into the Include field on the right side of the dialog, then click OK. After executing the node, the filtered data table is available at the output port of the Column Filter node.

To clean up your data, filtering out the rows corresponding to specific values of one column, use the Row Filter node. Search for the Row Filter node in the node repository on the left, add it to the workflow and connect it to the Column Filter node.

Open the Row Filter node configuration dialog and exclude rows from the input table where the column “country” has the value “unknown”. Click OK and execute the node. Now, the filtered data table is available at the output port of the Row Filter node.

To visualize the data, for example, building a stacked area chart and a pie chart, use the nodes Stacked Area Chart and Pie/Donut Chart. Search for them and add them to the workflow, connecting both to the Row Filter node.

Configure the column “date” as the x-axis column in the configuration dialog of the Stacked Area Chart node.

Configure the column “country” as category column, “Sum” as aggregation method, and “amount” as frequency column in the Pie/Donut Chart node.

Now you can execute and view the output of these last two nodes. Right-click the node and choose Execute and Open View from the context menu. A new window will open showing the charts you built with the sales data.

The input(s) are the data that the node processes via the node ports, and the output(s) are the resulting data. Each node has specific settings, which you can adjust in a configuration dialog.

Different types are represented by different node ports. Only ports of the same type indicated by the same color can be connected and are. Here are some examples of ports for frequently used data types.

A node can be in four different states. The node status is shown by a traffic light below each node.

A series of interconnected nodes defines a workflow. Nodes can be connected via their input and output ports to form a workflow. Once a workflow is executed the data inside the workflow flows then from left to right through the connections either step by step or entirely.

Components really are KNIME nodes that you create with a KNIME workflow. They encapsulate and abstract functionality, can have their own dialog, and can have their own sophisticated, interactive views. Components can be reused in your own workflows but also shared with others: via KNIME Server or KNIME Hub. They can also represent web pages in an analytical application deployed to others via KNIME Server.

Metanodes allow you to organize your workflows better: you can take part of a larger workflow and collapse it into a gray box that hides that part of the workflow’s functionality. It also makes it easier for others to understand what your workflow does as you can structure it a bit more hierarchically.