Search Results for author: Matthias Weidlich

Found 14 papers, 6 papers with code

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.

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

Knowledge-Driven Modulation of Neural Networks with Attention Mechanism for Next Activity Prediction

1 code implementation14 Dec 2023 Ivan Donadello, Jonghyeon Ko, Fabrizio Maria Maggi, Jan Mendling, Francesco Riva, Matthias Weidlich

Predictive Process Monitoring (PPM) aims at leveraging historic process execution data to predict how ongoing executions will continue up to their completion.

Activity Prediction Predictive Process Monitoring

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

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.

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.

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.

Privacy Preserving

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.

Privacy Preserving

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.

Clustering Node Classification +1

Detecting Rumours with Latency Guarantees using Massive Streaming Data

no code implementations13 May 2022 Thanh Tam Nguyen, Thanh Trung Huynh, Hongzhi Yin, Matthias Weidlich, Thanh Thi Nguyen, Thai Son Mai, Quoc Viet Hung Nguyen

Today's social networks continuously generate massive streams of data, which provide a valuable starting point for the detection of rumours as soon as they start to propagate.

Rumour Detection

Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs

no code implementations17 Jul 2022 Thanh Tam Nguyen, Thanh Cong Phan, Minh Hieu Nguyen, Matthias Weidlich, Hongzhi Yin, Jun Jo, Quoc Viet Hung Nguyen

Since the spread of rumours in social media is commonly modelled using feature-annotated graphs, we propose a query-by-example approach that, given a rumour graph, extracts the $k$ most similar and diverse subgraphs from past rumours.

Graph Representation Learning Rumour Detection

Large Process Models: Business Process Management in the Age of Generative AI

no code implementations2 Sep 2023 Timotheus Kampik, Christian Warmuth, Adrian Rebmann, Ron Agam, Lukas N. P. Egger, Andreas Gerber, Johannes Hoffart, Jonas Kolk, Philipp Herzig, Gero Decker, Han van der Aa, Artem Polyvyanyy, Stefanie Rinderle-Ma, Ingo Weber, Matthias Weidlich

The continued success of Large Language Models (LLMs) and other generative artificial intelligence approaches highlights the advantages that large information corpora can have over rigidly defined symbolic models, but also serves as a proof-point of the challenges that purely statistics-based approaches have in terms of safety and trustworthiness.

Management

Mining a Minimal Set of Behavioral Patterns using Incremental Evaluation

no code implementations5 Feb 2024 Mehdi Acheli, Daniela Grigori, Matthias Weidlich

Process mining provides methods to analyse event logs generated by information systems during the execution of processes.

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