Feature Question DecisionPointAnalysis

[On behalf of Anne Rozinat]

Hi Christoph,

If I understand you correctly, you would like to find out how earlier choices in the process influence some global process outcome? There are 3 things that come to my mind:


1) The Decision Miner analyzes the possible choices at a decision point. So, the target concept to be learned is about the decision at such a choice point (the alternative branches are the target classes).
If your target concept is something else, for example, some global outcome reflected in a data attribute (like: "success" vs. "no success"), then the Decision Miner cannot be directly used for your problem.

However, you could try to tweak your data in such a way that you simply include your global outcome as (a) an audit trail entry at the end of the process instance, whereas the 'WorkflowModelElement' part says either "success" or "no success", and (b) include a corresponding choice construct in the process model that reflects the different data attribute values, so the choice between two transitions "success" and "no success" that match the audit trail entries from (a).


2) If you do not want to analyze the impact of data attributes but the impact of the history of an instance itself, then you can try the 'Enhance Log with History' analysis plug-in, which adds data attributes reflecting the history of a case in each stage as a bag of actions. This is explained in the help text of the plug-in as follows:

"This plugin adds counting measures to the data part of each audit trail entry. For example, if a log trace contains the sequence ABAC, then there will be a new data attribute "#A" = "1", "#B" = "0", and "#C" = "0" for log event B, data attributes "#A" = "1", "#B" = "1", and "#C" = "0" for the second log event A, and the data attributes "#A" = "2", "#B" = "1", and "#C" = "0" for log event C etc. So, the history is captured in a simplified way without including the current log event itself. The purpose of this plugin is to enable the analysis of the history via data mining techniques, e.g., in the Decision Miner."


3) The goal of the Decision miner is to discover a decision tree, and to derive decision rules. So, inferring possible structural patterns to gain insight is the primary goal - but not prediction. However, if you can tweak your data to make the Decision Miner work for the rest of your problem, then you can export the data for each decision point to a format that can be analyzed by Weka, an open source Machine Learning workbench. There, you are likely to also find prediction algorithms that you might try?


I hope this helps.

Best regards,
Anne

[On behalf of Christoph Giesser]

Dear Anne, dear Wil,

thanks a lot for your recommendations and hints! Yes, I am interested in how earlier choices in the process influence the global process outcome, e.g. I'd like to be able to predict the outcome ("probably yes" / "probably no") at a given point in the process, based on previous process executions.

Tweaking the data so that it fits for ProM might be a solution, however, I'm rather interested whether a tool for this purpose already exists or not. Maybe Anne's proposal 2) is the right one for me, I'll have to check that. I already had a look at the proposed literature and find it very promising (esp. "Time Prediction based on Process Mining").

I just wonder whether there really is no literature on outcome prediction in business processes as I am still not sure which methodology to apply. ..

So, again, thanks a lot for your help! ProM still seems a useful tool for my thesis.

Regards,
Christoph