BPM Milan: Automating Knowledge Transfer

Michael Granitzer of Know-Centre Graz presented a paper on Automating Knowledge Transfer and Creation in Knowledge Intensive Business Processes, co-authored by Gisela Granitzer, Stefanie Lindstaedt, Andreas Rath also of Know-Center, Klaus Tochtermann of Graz Univeristy of Tecnology, and Wolfgang Groiss of m2n consulting (I know that I’m committing a big faux pas by rearranging the order of the authors, but it seems more logical for me to group them by organization).

The key issue is that the wealth of information about processes and best practices amongst users of systems is often never captured and used to feed back into process documentation or process improvement. Although it’s possible to use wikis and other social software to attempt to collect this information, the authors have devised automated mechanisms for gathering this information through detecting and documenting user interactions and tasks in a knowledge base, which can then be mined and analyzed by a process designer in order to feed back into the global process and its documentation.

The system captures the end-user’s activities (content and context) automatically by detecting events, grouping them into blocks, then into tasks. The task recognition itself is important, since it uses automated predictive classification techniques for recognizing tasks based on the events (now I’m in 1983 in a pattern recognition course 😉 ), and they’re achieving around 75% accuracy in their recognition rates. Note that these are not events and tasks executed in the context of a structured business process in a BPMS, but rather the use of any application available to the user in order to do their work: the web, MS-Office tools, etc. The classification methods were trained, in part, by a period of the users manually tagging their events as specific tasks.

On the mining and analysis side, they looked at process mining techniques such as the ProM framework, and explorative analysis techniques, but I have the sense that they haven’t been quite as successful in automating that side of things.

There are a number of concepts derived from this research, including that of tagging resources with tags, that is, being able to capture knowledge of which users perform which tasks.

They plan to continue on with the research, which will include fine tuning of task detection, and enhancing the classification methods to allow grouping of task groups into processes.

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