In the latest Process Mining Café, Rudi and I spoke with Scott Leslie from Evidant about analysis transformations.
The data that you find in information systems was not created for process mining purposes. So, often, you need to shape the data to make it suitable. Furthermore, different analysis questions may require particular views on the data. We look at two concrete examples to illustrate the steps in such transformations.
You can now watch the recording here if you missed the live broadcast or want to re-watch the café. Thanks again to Scott and all of you for joining us!
Links
Here are the links that we mentioned during the session:
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Previous Process Mining Café about common data preparation questions for process mining
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Our Process Mining Transformations series shows common data transformations, such as unfolding loops for activity repetitions
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Process mining examples for customer journey analyses
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If you have a start and complete timestamp for each activity, you can distinguish active time and waiting time between activities
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Another example of mapping activity names to bridge the business-IT gap in the data
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Keep in mind that attributes are not static but may change over time. If you enhance your data with such attributes today, you may be missing their historical values
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Leonard from the City of Lausanne shows in his Process Mining Camp talk how he tried to group free-text activities
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Enhancing your data set can enable you to take different views, like hand-over of work between different departments
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We showed how to focus on steps between activities, and Marcus asked what to do about missing activities
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Example of analysis transformation driven by domain knowledge: Fran from the UW Health Nursing Informatics Department shares one of her data transformations
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