For the first of the paper in the session on Flexibility and User Interaction, Helen Schonenberg of Eindhoven University of Technology presented a paper on Supporting Flexible Processes Through Log-Based Recommendations, co-authored by Barbara Weber, Boudewijn van Dongen and Wil van der Aalst.
This is related to the research on automated process mining (or process discovery) based on system logs, which is similar to the type of work being done by Fujitsu with their process discovery product/service
She started by discussing recommender systems, such as we are all familiar with from sites like Amazon: the user provides some input, and based on their past behavior and that of users who are similar in some way, the system recommends items. Recommendation algorithms are based on filtering of user/item matrices and aggregation of the results.
In the case of a process recommender, there is a key goal such as minimizing throughput time; from this and a filtered view of the history log of this and other users’ past performance, a next step can be recommended. Their current work is focused on fine-tuning the filtering algorithms by which the possible paths in the log are filtered for use as recommendations, and the weighted aggregation algorithms.
She walked us through their experimental setup and results, and showed that it is possible to improve processes by the use of runtime recommendations, in the case where users have the choice of which activity to execute next. This can be used in any system that has a logging system and uses a worklist for task presentation.