Improved learning on prior classification feedback

Improved learning on prior classification feedback

The Onedot platform module for product categorisation has been further improved in terms of learning rate.

Feedback on classified products is now used even further and in various other scenarios.

Further improvements for search/replace

Further improvements for search/replace

Search/replace in data sets is now also possible for columns with multiple values.

In addition, the display of the found values has been further optimised with little column width.

Multiple parallel onboarding steps

Multiple parallel onboarding steps

The progress view now also shows several steps that are in progress at the same time.

In product data onboarding that uses category-independent target attributes, for example, attribute mapping can be done in parallel with product categorisation.

The same applies to product data catalogues or target definitions that use several independent classification systems.

Maximum number of values in feedback tasks

Maximum number of values in feedback tasks

The Onedot app now offers a condition in feedback tasks that checks how many values are used in a cell.

This also allows scenarios to be modelled where target attributes such as the connections of a device should only have a maximum number of connections in the product database.

Improved support for Akeneo PIM

Improved support for Akeneo PIM

Further improvements regarding the integration in Akeneo PIM were realised in the Onedot platform.

In addition to further support for Akeneo PIM Enterprise Edition, the simplified handling of amounts/mass units and smooth import into Akeneo PIM is now also possible.

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