There is lots of academic interest in process mining — the use of tools to evaluate events and determine flow patterns (processes) and bottlenecks. Recently a group of university people announced the free availability of Nirdizati — an Alpha release of a predictive monitoring tool that individuals and organizations can experiment with. http://nirdizati.cs.ut.ee Use of process mining isn't for the casual users, but if you are a student studying process mining and are willing to work a bit to explore a new type of software, it might be worth checking out.
The authors explain Nirdizati as follows: a dashboard-based monitoring tool, which is updated periodically based on incoming streams of events. However, unlike classical monitoring dashboards, Nirdizati does not focus on showing the current state of business process executions, but their future state (e.g. when will each case finish). On the backend. Nirdizati uses predictive models trained using deep learning methods. A paper about these methods will be presented at the upcoming CAiSE'2017 conference in June 2017 (the paper and the associated Python code is available here: https://verenich.github.io/ProcessSequencePrediction/ )
