Search Results for author: Matthias Weidlich

Found 8 papers, 4 papers with code

SaCoFa: Semantics-aware Control-flow Anonymization for Process Mining

1 code implementation17 Sep 2021 Stephan A. Fahrenkrog-Petersen, Martin Kabierski, Fabian Rösel, Han van der Aa, Matthias Weidlich

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.

A Distance Measure for Privacy-preserving Process Mining based on Feature Learning

no code implementations14 Jul 2021 Fabian Rösel, Stephan A. Fahrenkrog-Petersen, Han van der Aa, Matthias Weidlich

To avoid this and incorporate the semantics of activities during anonymization, we propose to instead incorporate a distance measure based on feature learning.

Entropia: A Family of Entropy-Based Conformance Checking Measures for Process Mining

no code implementations21 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.

Partial Order Resolution of Event Logs for Process Conformance Checking

1 code implementation5 Jul 2020 Han van der Aa, Henrik Leopold, Matthias Weidlich

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.

Unsupervised Learning of Node Embeddings by Detecting Communities

no code implementations25 Sep 2019 Chi Thang Duong, Dung Hoang, Truong Giang Le Ba, Thanh Le Cong, Hongzhi Yin, Matthias Weidlich, Quoc Viet Hung Nguyen, Karl Aberer

We provide empirical evidence that the communities learned by DMC are meaningful and that the node embeddings are competitive in different node classification benchmarks.

Node Classification Node Clustering

Parallel Computation of Graph Embeddings

no code implementations6 Sep 2019 Chi Thang Duong, Hongzhi Yin, Thanh Dat Hoang, Truong Giang Le Ba, Matthias Weidlich, Quoc Viet Hung Nguyen, Karl Aberer

We therefore propose a framework for parallel computation of a graph embedding using a cluster of compute nodes with resource constraints.

Graph Embedding

Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring

1 code implementation23 May 2019 Stephan A. Fahrenkrog-Petersen, Niek Tax, Irene Teinemaa, Marlon Dumas, Massimiliano de Leoni, Fabrizio Maria Maggi, Matthias Weidlich

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.

Predictive Process Monitoring

CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers

1 code implementation8 Jan 2019 Alexandros Koliousis, Pijika Watcharapichat, Matthias Weidlich, Luo Mai, Paolo Costa, Peter Pietzuch

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.

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