On the Foundations of dynamic coalitions: modeling changes and evolution of workflows in healthcare scenarios

Dynamic Coalitions denote a temporary collaboration between different entities to achieve a common goal. A key feature that distinguishes Dynamic Coalitions from static coalitions is Dynamic Membership, where new members can join and others can leave after a coalition is set. This thesis studies workflows in Dynamic Coalitions, by analyzing their features, highlighting their unique characteristics and similarities to other workflows, and investigating their relation with Dynamic Membership. For this purpose, we use the formal model of Event Structures and extend it to faithfully model scenarios taken as use cases from healthcare. Event Structures allow for workflow modeling in general, and for modeling Dynamic Membership in Dynamic Coalitions as well through capturing the join and leave events of members. To this end, we first extend Event Structures with Dynamic Causality to address the dynamic nature of Dynamic Coalitions. Dynamic Causality allows some events to change the causal dependencies of other events in a structure. Then, we study the expressive power of the resulting Event Structures and show that they contribute only to a specific kind of changes in workflows, namely the pre-planned changes. Second, we present Evolving Event Structures in order to support ad-hoc and unforeseen changes in workflows, as required by the use cases. Evolving Event Structures connect different Event Structures with an evolution relation which allows for changing an Event Structure during a system run. We consider different approaches to model evolution and study their relation. Furthermore, we show that the history of a workflow should be preserved in our case of evolution in Dynamic Coalitions, and we allow for extracting changes from an evolution to support Process Learning. Third, to capture the goals of Dynamic Coalitions, we equip Evolving Event Structures with constraints concerning the reachability of a set of events that represents a goal. The former extensions allow for examining the changes and evolutions caused by members, and examining members’ contributions to goal satisfaction, through their join and leave events. Finally, we highlight many modeling