Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
A Spike-Train Probability Model
Neural Computation
Online Interaction Analysis Framework for Ad-Hoc Collaborative Processes in SOA-Based Environments
Transactions on Petri Nets and Other Models of Concurrency II
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This work was motivated by a recent experience where we needed to develop enterprise operational reports when the underlying business process is not entirely known, a common situation for large companies with sophisticated IT systems. We learned that instead of relying on human knowledge or business documentation, it is much more reliable to learn from the flow structure of event sequences recorded for work items. An example of work items are product alarms detected and reported to a technical center through a remote monitoring system; the corresponding event sequence of a work item is an alarm history, i.e. the alarm handling process. We call the flow of event sequences recorded for work items, workflow. In this paper, we developed an algorithm to discover and visualize workflows for data from a remote technical support center, and argue that workflow discovery is a prerequisite for rigorous performance analysis. We also carried out a detailed performance analysis based on the discovered workflow. Among other things, we find that service time (e.g. the time necessary for handling a product alarm) fits the profile of a log-mixture distribution. It takes at least two parameters to describe such a distribution, which leads to the proposed method of using two metrics for service time reporting.