Abstract

In this paper, we explore the potential of probabilistic process mining for Enterprise Collaboration Systems (ECS) to predict the behavior of systems users. Towards this objective, we discuss applicability, limitations and challenges of probabilistic process mining in the context of ECS. We argue that probabilistic process mining can be a valuable method for researchers as well as practitioners (managers of collaboration platforms). We create and examine two process models using a probabilistic finite automata algorithm on event data of an enterprise collaboration system to show the feasibility of probabilistic process mining in ECS. Our research illustrates the most probable sequence of user activities in the selected system and demonstrates a way to predict the communities that a user will likely be active in.

Share

COinS
 

Predicting User Interaction in Enterprise Social Systems Using Process Mining

In this paper, we explore the potential of probabilistic process mining for Enterprise Collaboration Systems (ECS) to predict the behavior of systems users. Towards this objective, we discuss applicability, limitations and challenges of probabilistic process mining in the context of ECS. We argue that probabilistic process mining can be a valuable method for researchers as well as practitioners (managers of collaboration platforms). We create and examine two process models using a probabilistic finite automata algorithm on event data of an enterprise collaboration system to show the feasibility of probabilistic process mining in ECS. Our research illustrates the most probable sequence of user activities in the selected system and demonstrates a way to predict the communities that a user will likely be active in.