This paper proposes a workflow intelligence and quality improvement framework maximizing the workflow trace- ability and rediscoverability by analyzing the total sequences of the control-path perspective of a workflow model and by rediscovering their runtime enactment history from the workflow log information. The framework needs two kinds of algorithms - One is for generating the total sequences of the control-paths from a workflow model, and the other is for rediscovering the runtime enactment history of each control-path out of the total sequences from the corresponding workflow's execution logs. Eventually, these rediscovered knowledge and history of a workflow model make up a control-path oriented intelligence of the workflow model, which ought to be an essential ingredient for improving the quality of the workflow model. Based upon the workflow intelligence, it is possible for the workflow model to be gradually refined and finally maximize its quality by repeatedly redesigning, reengineering and/or refining during its whole life-long time period. And, this paper describes not only propose of control-path oriented mining algorithm but also implementation of control-path oriented workflow intelligence analysis and mining system.
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