Talks and Poster Presentations (with Proceedings-Entry):
C. Dorn, C. Marin, N. Mehandjiev, S. Dustdar:
"Self-learning Predictor Aggregation for the Evolution of People-Driven Ad-Hoc Processes";
Talk: Business Process Management 9th International Conference (BPM 2011),
- 2011-09-02; in: "Business Process Management Proceedings of the 9th International Conference (BPM 2011)",
S. Rinderle-Ma, F. Toumani, K. Wolf (ed.);
Contemporary organisational processes evolve with peopleīs
skills and changing business environments. For instance, process documents
vary with respect to their structure and occurrence in the process.
Supporting users in such settings requires sophisticated learning mechanisms
using a range of inputs overlooked by current dynamic process
systems. We argue that analysing a documentīs semantics is of uttermost
importance to identify the most appropriate activity which should
be carried out next. For a system to reliably recommend the next steps
suitable for its user, it should consider both the process structure and the
involved documentsī semantics. Here we propose a self-learning mechanism
which dynamically aggregates a process-based document prediction
with a semantic analysis of documents. We present a set of experiments
testing the prediction accuracy of the approaches individually then compare
them with the aggregated mechanism showing a better accuracy.
document analysis, process recommendation, people-driven ad-hoc processes, document and process evolution.
Project Head Schahram Dustdar:
Created from the Publication Database of the Vienna University of Technology.