Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Computational intelligence: a logical approach
Computational intelligence: a logical approach
ACM Computing Surveys (CSUR)
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Rediscovering workflow models from event-based data using little thumb
Integrated Computer-Aided Engineering
Detecting implicit dependencies between tasks from event logs
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
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The notion of abductive workflow mining is introduced, which refers to the process of discovering important workflows from event logs that are believed to cause or explain certain behaviour. The approach is based on the notion of abductive reasoning, where hypotheses are found that, if added to a rule base, would necessarily cause an observation to be true. We focus on the instance of workflow mining where there are critical tasks in the underlying process that, if observed, must be scrutinized more diligently to ensure that they are sufficiently motivated and executed under acceptable circumstances. Abductive workflow mining is then the process of determining activity that would necessarily imply that the critical activity should take place. Whenever critical activity is observed, one can then inspect the abductive workflow to ascertain whether there was sufficient reason for the critical activity to occur. To determine such workflows, we mine recorded log activity for task successor rules, which indicate which tasks succeed other tasks in the underlying process. Binary resolution is then applied to find the abductive explanations for a given activity. Preliminary experiments show that relatively small and concise abductive workflow models can be constructed, in comparison with constructing a complete model for the entire log.