no code implementations • 25 May 2017 • Niek Tax, Emin Alasgarov, Natalia Sidorova, Wil M. P. van der Aalst, Reinder Haakma
Refinements of sensor level event labels suggested by domain experts have been shown to enable discovery of more precise and insightful process models.
no code implementations • 25 May 2017 • Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst
However, events recorded in smart home environments are on the level of sensor triggers, at which process discovery algorithms produce overgeneralizing process models that allow for too much behavior and that are difficult to interpret for human experts.
no code implementations • 10 Oct 2016 • Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst, Reinder Haakma
Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i. e. subsets of possible events are taken into account to create so-called local process models.
no code implementations • 12 Sep 2016 • Niek Tax, Emin Alasgarov, Natalia Sidorova, Reinder Haakma
Finding the right event labels to enable application of process mining techniques is however far from trivial, as simply using the triggering sensor as the label for sensor events results in uninformative models that allow for too much behavior (overgeneralizing).
no code implementations • 23 Jun 2016 • Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst
We show that when process discovery algorithms are only able to discover an unrepresentative process model from a low-level event log, structure in the process can in some cases still be discovered by first abstracting the event log to a higher level of granularity.
no code implementations • 23 Jun 2016 • Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst
We present a statistical evaluation method to determine the usefulness of a label refinement for a given event log from a process perspective.
no code implementations • 20 Jun 2016 • Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst
The technique presented in this paper is able to learn behavioral patterns involving sequential composition, concurrency, choice and loop, like in process mining.