KNIME Analytics Platform User Guide

The entry page is displayed by clicking the Home tab.

Here you will find:

The workflow editor is where workflows are assembled. Workflows are made up of individual tasks, represented by nodes.

One way to create a new workflow is to go to the space explorer, click the three dots and select Create workflow from the menu. Give the workflow a name and click Create.

In the new empty workflow editor, create a workflow by dragging nodes from the node repository to the workflow editor, then connecting, configuring, and executing them.

By default you can connect to your account on KNIME Community Hub from the Home tab.

It is possible to add a new KNIME Hub instance mount point by clicking Preferences, in the top right corner of the user interface.

Go to KNIME Explorer section and click New…​. In the window that opens select KNIME Hub and add your Hub URL. Then click Apply.

Now the new mount point will show up in the Home tab.

Sign in and select the space you want to work on. The content of the space and the related operations you can do on the items are visible in the space explorer.

You can switch back to the classic KNIME Analytics Platform user interface under Menu, in the top right corner of the user interface, and select Switch to classic user interface.

You can switch back to KNIME Modern UI at any time by pressing the button Open KNIME Modern UI in the classic user interface, at the top right corner.

The space explorer is where you can manage workflows, folders, components and files in a space, either local or remote on a KNIME Hub instance. A space can be:

You can switch to other spaces by:

In the space explorer you can see:

Double click a new empty workflow to open it in the workflow canvas and start adding nodes to the canvas from the node repository.

Here you can click the three dots to select one of the following actions within the current space:

You can also drop files to the canvas. KNIME will create the appropriate file reading node automatically and preconfigure it. Finally you can drop a component to the canvas to use the component in the current workflow.

Select an item from the current space and right click on it to access the item context menu. From here you can Rename, Delete, Export (only available for workflows), Upload (if you are already connected to one of the available Hub mount points) or Connect.

When you create a new workflow, the canvas will be empty.

To build the workflow you will need to add nodes to it by dragging them from the node repository and connecting them. Alternatively you can drag an output port of a node to show the workflow coach which will suggest you the compatible nodes and directly connect them.

Once two nodes are added to the workflow editor, they can be connected by clicking the output port of the first node and release the mouse at the input port of the second node. Now, the nodes are connected. For some nodes you might have the ability to add specific ports. When hovering over these nodes you will see a + sign appearing. Click it to add a port. If the nodes supports different types of these dynamic ports a list will appear for you to scroll down to select the type of port you want to add.

You can also add a node between two nodes in a workflow. To do so drag the node from the node repository, and release it at its place in the workflow.

Currently installed nodes are available in the node repository. You can add a node from the node repository into the workflow editor by drag and drop, as explained in the section Building Workflows.

Search for a node by typing a search term in the search field on top of the node repository, as shown in Figure 18.

By default a specific set of nodes to help you get started with the KNIME Analytics Platform will be shown. You can expand the search results by changing the filter settings of the node repository. Click the icon in the node repository to go to the KNIME Modern UI preferences page and change the default of the nodes included in the node repository search results.

You can view your node repository as icon or list view. To switch between the two states click (icon view) or (list view).

You can access the node description, with information about the node function, the node configuration and the different ports available for the node in the following ways:

The description panel on the left of the KNIME Analytics Platform provides a description of the currently active workflow, or a selected component.

Click the pen icon to change the workflow description, add links to external resources and add tags.

KNIME features an AI assistant designed to efficiently answer your queries about the KNIME platform and assist in constructing customized workflows, simplifying your data analysis tasks.

The Node Monitor tab is located on the bottom part of the user interface shown in Figure 28. It is especially useful to inspect intermediate output tables in the workflow.

Here you can choose to show the flow variables or a preview of the output data at any port of a selected node in the active workflow.

Switch to Statistics in order to see some basic statistics of the data.

You can also detach the table or the statistics view and open it in a new window. To do so click the icon in the respective view (Table or Statistics). This allows you to open multiple table or statistics for multiple nodes in your workflow.

Read more about the data table shown in the node monitor in the KNIME tables section.

By clicking the Help button in the top right corner of the user interface you can see multiple useful links like:

When a node is reset, the node status changes from "executed" to "configured" and the output of the node is not available anymore. When saving a workflow in an executed state, the data used in the workflow are saved as well. That is, the larger the dataset, the larger the file size. Therefore, resetting workflows before saving them is recommended in case the dataset can be accessed without any restrictions.

A reset workflow only saves the node configurations, and not any results. However, resetting a node does not undo the operation executed before. All operations done during creation, configuration, and execution of a workflow are reported in the knime.log file.

Click Menu > Show KNIME log in File Explorer in the top right corner of the user interface to see the knime.log location folder. The knime.log file has a limited size, and after reaching it the rows will be overwritten from the top.

With the release of KNIME Analytics Platform version 5.1 the preferences have been rearranged. They can be open, from the Modern User Interface by clicking Preferences in the top right corner of the Analytics Platform.

Here, a list of subcategories is displayed in the dialog that opens. Each category contains a separate dialog for specific settings like database drivers, available update sites, and appearance.

When installing KNIME Analytics Platform, configuration options are set to their defaults. The configuration options, i.e. options used by KNIME Analytics Platform, range from memory settings to system properties required by some extensions.

You can change the default settings in the knime.ini file. The knime.ini file is located in the installation folder of KNIME Analytics Platform.

Edit the knime.ini file with any plaintext editor, such as Notepad (Windows), TextEdit (MacOS) or gedit (Linux).

The entry -Xmx1024m in the knime.ini file specifies how much memory KNIME Analytics Platform is allowed to use. The setting for this value will depend on how much memory is available in the running machine. We recommend setting it to approximately one half of the available memory, but this value can be modified and personalized. For example, if the computer has 16GB of memory, the entry might be set to -Xmx8G.

Besides the memory available, you can define many other settings in the knime.ini file. Find an overview of some of the most common settings in Table 1 or in this complete list of the configuration options.

KNIME’s runtime behavior can be configured in various ways by passing options on the command line during startup. Since KNIME is based on Eclipse, all Eclipse runtime options also apply to KNIME.

KNIME also adds additional options, which are described below.

Very common input and output ports of nodes are data input ports and data output ports, which correspond to the black triangles in Figure 33.

A data table is organized by columns and rows, and it contains a number of equal-length rows. Elements in each column must have the same data type.

The data table shown in Figure 34 is produced by a CSV Reader node, which is one of the many nodes with a black triangle output port for data output. To open the table, click the node. Execute the node if it is not yet execute. The table will show up in the node monitor.

The output table has row numbers, unique RowIDs and column headers. The RowIDs are automatically created by the reader node, but they can also be defined manually. The RowIDs and the column headers can therefore be used to identify each data cell in the table. Missing values in the data are shown by a red question mark in a circle.

At the top of the node monitor you can select which output port you want to view via the tabs and the flow variable tab, which shows the available flow variables in the node output and their current values. Next row will indicate the table dimensions, meaning how many rows and how many columns there are in the table at that specific output port. Here you can also use the toggle to switch to Statistics. This tab shows the meta information of the table, like the columns names, columns types and some other statistics data.

The basic data types in KNIME Analytics Platform are Integer, Double, and String, along with other supported data types such as Long, Boolean value, JSON, URI, Document, Date&Time, Bit vector, Image, and Blob. KNIME Analytics Platform also supports customized data types, for example, a representation of a molecule.

Switch to the Statistics view in an output table, to see the data types of the columns in the data table, as shown in Figure 35. For numerical values, only the range of the values in the data is shown. For string values, the different values appearing in the data are shown.

The reader nodes in KNIME Analytics Platform assign a data type to each column based on their interpretation of the content. If the correct data type of a column is not recognized by the reader node, the data type can be corrected afterwards. There are nodes available to convert data types. For example: String to Number, Number to String, Double to Int, String to Date&Time, String to JSON, and String to URI.

Many of the special data types are recognized as String by the reader nodes. To convert these String columns to their correct data types, use the Column Type Auto Cast node.

When you use the File Reader node to read a file you can convert the column types directly via the node configuration dialog. To do so go to the Transformation tab in the configuration dialog and change the type of the desired column, as shown in Figure 36.

Rows in the table view output can be sorted by values in one column by clicking the up (ascending) and down (descending) arrow that appears hovering over the column name in the header. Note that this sorting only affects the current output view and has no effect on the node output.

To sort rows in an output table permanently, use the Sorter node. Use the Column Resorter node to reorder columns.

In a table view output, you can also change the way in which numeric values are displayed in a data table. For example, it is possible to display numeric values as percentages, with full precision, or replace digits by a grey scale or bars. To see these and other rendering options for a column, click the carat icon in the column header, and select the desired available renderer, as shown in Figure 37. Note that these changes are temporary and have no effect on the node output.

When executed, many KNIME nodes generate and provide access to tabular data at their output ports. These tables might be small or large and, therefore, might fit into the main memory of the executing machine or not. Several options are available for configuring which tables to hold in memory as well as when and how to write tables to disk. These options are outlined in this section.