## 1 Introduction  
A workflow (or business process) management system can support two fundamental functionalities, namely, the modeling functionality and the enacting functionality. The modeling functionality allows modelers to define, analyze, and maintain the workflow processes by hooking all essential workflow entities, such as activities, roles, performers, relevant data, and invoked applications, on the corresponding procedures. By contrast, the enacting functionality supports performers to play the essential roles of invoking, executing, and monitoring all instances of the workflow processes. The logical foundation of such a workflow management system is based on its underlying workflow model, which implies that the system is able to automate the defining, creation, execution, and management of the workflow processes according to the internal principle and structure of the underlying workflow model. To date, several workflow models [1; 2] have been proposed, almost all of which employ five essential entity types, namely, the activity, role, performer, data repository, and application, to represent organizational works and their procedural collaborations. In this study, we focused on the performer entity type. In recent years, studies on workflow started focusing on people working in workflow-supported organizations because it is widely accepted for a workflow system to be a people system. By analyzingthe interactive and collaborative behaviors among people involved in performing workflow processes, we are able to measure and estimate their overall performance in real businesses, as well as their work productivity. For more than a decade, workflow mining [3,4] has received significant attention as a key enabling technology for acquiring human-centered knowledge regarding workflow processes. In this regard, the authors' research group has proposed research and development issues when applying the concept of social networks and analysis methods into human-centered workflow knowledge discovery and analysis. Under this context, we are particularly interested in the work transference network [5] among performers in a workflow-supported organization. More specifically, this network is established through work transference (or handover) relationships between two performers in charge of the preceding and following activities within the workflow process. As shown in Figure 1, there are two performers, \([PERSON]\) and \([PERSON]\), in charge of the consecutive activities \(A\) and \(B\), respectively. As the predecessor of this case, \([PERSON]\) will transfer the results of the execution of \(A\) to his successor \([PERSON]\). Therefore, this type of relationship reflects the relevancy and intensity between performers in terms of working, and accordingly, can eventually be an important analytical property for the acquisition of human-centered workflow knowledge. In research on the work transference network, two main branches exist: discovery and rediscovery. The former is to discover a work transference network through analyzing a specific workflow model, whereas the latter is concerned with mining a work transference network from workflow event logs of the model. More specifically, we differentiate the former from the latter; the former is used to explore a planned work transference network [5], whereas, the latter is to explore an enacted work transference network. This paper is directly related to work transference network rediscovery. Ultimately, through these discovery and rediscovery concepts, it is possible to assess the workflow fidelity, which indicates how faithfully the observed work transference network (rediscovery) reflects the planned network (discovery). Through the generalization of workflow fidelity, we can answer the following managerial questions. * Who is the performer most closely working with a particular performer? * Based on work transferences, what is the highly recommended performer group in specific procedural working steps within the workflow process?