Privacy-preserving process mining enables the analysis of business processes using event logs, while giving guarantees on the protection of sensitive information on process stakeholders.
To avoid this and incorporate the semantics of activities during anonymization, we propose to instead incorporate a distance measure based on feature learning.
no code implementations • 21 Aug 2020 • Artem Polyvyanyy, Hanan Alkhammash, Claudio Di Ciccio, Luciano García-Bañuelos, Anna Kalenkova, Sander J. J. Leemans, Jan Mendling, Alistair Moffat, Matthias Weidlich
This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mining founded on the notion of entropy from information theory.
A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance.
We provide empirical evidence that the communities learned by DMC are meaningful and that the node embeddings are competitive in different node classification benchmarks.
We therefore propose a framework for parallel computation of a graph embedding using a cluster of compute nodes with resource constraints.
Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances.
Systems such as TensorFlow and Caffe2 train models with parallel synchronous stochastic gradient descent: they process a batch of training data at a time, partitioned across GPUs, and average the resulting partial gradients to obtain an updated global model.