BPM Milan: Social Software for Modeling Business Processes

Agnes Koschmider of the Institute of Applied Informatics and Formal Description Methods (Universitat Karlsruhe) presented the next paper on Social Software for Modeling Business Processes, co-authored by Minseok Song and Hajo Reijers of Eindhoven University.

I’m transported back to 1981, sitting in a graph theory lecture in university: this is a graph theory approach to social networks in order to provide recommendations during process modeling. The technique is for recommending process fragments from a process repository to someone during modeling (where the differences between the process fragments are the people who perform them, not the structure of the process itself): suggesting the performers to assign to a specific process fragment based on the past interactions between those people and the ones already assigned to tasks in the model.

In order to do this, it’s first necessary to derive the social network (graph) between users: how they’re connected based on their past history in process instances, through transfer of work as part of a structure process flow, subcontracting (delegation) of work, and cooperation (how often two performers do the same activity in a process). It’s also possible to derive the social network based on recommendation history. Once the metrics of the social network connectivity are gathered, the distance between each set of performers can be measured using a measurement such as Hamming or Minkowski distance.

Although the underlying mathematics are complex, the idea is to reduce the complexity for the process modeler by providing recommendations on which process fragment in a repository would help to create the most effective process.

Aside from the setting and content, the humor at academic conferences is much different as well: when the use of Petri Nets as a modeling paradigm at one particular university was described as a “political issue”, it got the biggest laugh of the day. 🙂

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.