3 research outputs found
Exploiting Event Log Event Attributes in RNN Based Prediction
In predictive process analytics, current and historical process data in event logs are used to predict future. E.g., to predict the next activity or how long a process will still require to complete. Recurrent neural networks (RNN) and its subclasses have been demonstrated to be well suited for creating prediction models. Thus far, event attributes have not been fully utilized in these models. The biggest challenge in exploiting them in prediction models is the potentially large amount of event attributes and attribute values. We present a novel clustering technique which allows for trade-offs between prediction accuracy and the time needed for model training and prediction. As an additional finding, we also find that this clustering method combined with having raw event attribute values in some cases provides even better prediction accuracy at the cost of additional time required for training and prediction.Peer reviewe
Conformance Checking of Mixed-paradigm Process Models
Mixed-paradigm process models integrate strengths of procedural and
declarative representations like Petri nets and Declare. They are specifically
interesting for process mining because they allow capturing complex behaviour
in a compact way. A key research challenge for the proliferation of
mixed-paradigm models for process mining is the lack of corresponding
conformance checking techniques. In this paper, we address this problem by
devising the first approach that works with intertwined state spaces of
mixed-paradigm models. More specifically, our approach uses an alignment-based
replay to explore the state space and compute trace fitness in a procedural
way. In every state, the declarative constraints are separately updated, such
that violations disable the corresponding activities. Our technique provides
for an efficient replay towards an optimal alignment by respecting all
orthogonal Declare constraints. We have implemented our technique in ProM and
demonstrate its performance in an evaluation with real-world event logs.Comment: Accepted for publication in Information System