BPM Milan: Visual Support for Work Assignment

Massimiliano de Leoni presented a paper on Visual Support for Work Assignment in Process-Aware Information Systems, co-authored by Wil van der Aalst and Arthur ter Hofstede.

This is relevant for systems with worklists, where it may not be clear to the user which work item to select based on a variety of motivations. In most commercial BPMS, a worklist contains just a list of items, each with a short description of some sort; he is proposing a visual map of work items and resources, where any number of maps can be defined based on different metrics. In such a map, the user can select the work item based on its location on the map, which represents its suitability for processing by that user at that time.

He walked us through the theory behind this, then the structure of the visualization framework as implemented. He walked us through an example of how this would appear on a geographic map, which was a surprise to me: I was thinking about more abstract mapping concepts, but he had a geographic example that used a Google map to visualize the location of the resources (people who can process the work item) and work items. He also showed a timeline map, where work items were positioned based on time remaining to a deadline.

Maybe I’m just not a visual person, but I don’t see why the same information can’t be conveyed by sorting the worklist (where the user would then choose the first item in the list as being the highest recommendation), although future research in turning a time-lapse of the maps into a movie for process mining is a cool concept.

BPM Milan: Supporting Flexible Processes Through Log-Based Recommendations

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.

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.