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Knowledge Graph Modularization for Cyber-Physical Production Systems

Stefan Bachhofner, Elmar Kiesling, Kabul Kurniawan, Emanuel Sallinger, Philipp Waibel
2021 International Semantic Web Conference  
Knowledge Graphs (KGs) have a large potential to support analytics and AI applications in the manufacturing domain, which in the age of Industry 4.0 is increasingly driven by data. Compared to other domains, however, KG techniques have seen limited adoption in this field so far. We argue that the construction of KGs in the context of Cyber-physical Production Systems (CPPSs) requires a systematic methodology grounded in domain-specific abstractions. Consequently, we introduce a KG
more » ... framework based on the well-established RAMI 4.0 architecture model. A key benefit of the proposed approach is that the resulting KGs support navigation across abstraction hierarchies, enabling bottom-up contextualization of raw data on the one hand, and top-down explanations by linking to lower levels of granularity on the other hand. We motivate the proposed approach and illustrate its application with a real-world use case from the automotive sector.
dblp:conf/semweb/BachhofnerKKSW21 fatcat:4apluuiuc5f4xoomnusnfk6gn4

Foundational Oracle Patterns: Connecting Blockchain to the Off-chain World [article]

Roman Mühlberger, Stefan Bachhofner, Eduardo Castelló Ferrer, Claudio Di Ciccio, Ingo Weber, Maximilian Wöhrer, Uwe Zdun
2020 arXiv   pre-print
Blockchain has evolved into a platform for decentralized applications, with beneficial properties like high integrity, transparency, and resilience against censorship and tampering. However, blockchains are closed-world systems which do not have access to external state. To overcome this limitation, oracles have been introduced in various forms and for different purposes. However so far common oracle best practices have not been dissected, classified, and studied in their fundamental aspects.
more » ... this paper, we address this gap by studying foundational blockchain oracle patterns in two foundational dimensions characterising the oracles: (i) the data flow direction, i.e., inbound and outbound data flow, from the viewpoint of the blockchain; and (ii) the initiator of the data flow, i.e., whether it is push or pull-based communication. We provide a structured description of the four patterns in detail, and discuss an implementation of these patterns based on use cases. On this basis we conduct a quantitative analysis, which results in the insight that the four different patterns are characterized by distinct performance and costs profiles.
arXiv:2007.14946v1 fatcat:vfcwtvdwubehbpcc4a7b72f56m

Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery

Stefan Bachhofner, Ana-Maria Loghin, Johannes Otepka, Norbert Pfeifer, Michael Hornacek, Andrea Siposova, Niklas Schmidinger, Kurt Hornik, Nikolaus Schiller, Olaf Kähler, Ronald Hochreiter
2020 Remote Sensing  
We studied the applicability of point clouds derived from tri-stereo satellite imagery for semantic segmentation for generalized sparse convolutional neural networks by the example of an Austrian study area. We examined, in particular, if the distorted geometric information, in addition to color, influences the performance of segmenting clutter, roads, buildings, trees, and vehicles. In this regard, we trained a fully convolutional neural network that uses generalized sparse convolution one
more » ... solely on 3D geometric information (i.e., 3D point cloud derived by dense image matching), and twice on 3D geometric as well as color information. In the first experiment, we did not use class weights, whereas in the second we did. We compared the results with a fully convolutional neural network that was trained on a 2D orthophoto, and a decision tree that was once trained on hand-crafted 3D geometric features, and once trained on hand-crafted 3D geometric as well as color features. The decision tree using hand-crafted features has been successfully applied to aerial laser scanning data in the literature. Hence, we compared our main interest of study, a representation learning technique, with another representation learning technique, and a non-representation learning technique. Our study area is located in Waldviertel, a region in Lower Austria. The territory is a hilly region covered mainly by forests, agriculture, and grasslands. Our classes of interest are heavily unbalanced. However, we did not use any data augmentation techniques to counter overfitting. For our study area, we reported that geometric and color information only improves the performance of the Generalized Sparse Convolutional Neural Network (GSCNN) on the dominant class, which leads to a higher overall performance in our case. We also found that training the network with median class weighting partially reverts the effects of adding color. The network also started to learn the classes with lower occurrences. The fully convolutional neural network that was trained on the 2D orthophoto generally outperforms the other two with a kappa score of over 90% and an average per class accuracy of 61%. However, the decision tree trained on colors and hand-crafted geometric features has a 2% higher accuracy for roads.
doi:10.3390/rs12081289 fatcat:lc2teaf4ljgqzhutjgglabo2wa

Towards a Process-oriented Analysis of Blockchain Data (invited paper)

Claudio Di Ciccio
2020 Modellierung  
The author is also grateful to Stefan Bachhofner, Dominik Felix, Dominik Haas, and Roman Mühlberger for their investigations and active collaboration.  ... 
dblp:conf/modellierung/Ciccio20 fatcat:zairm7uuxvagjbfl5k7virkjwy

Poster Presentations

2012 Hepatology  
Boehme, John Chiang 726: Norursodeoxycholic Acid Counteracts Hepatic Acute Phase Response via Modulation of NFκB Signaling Emina Halilbasic, Thierry Claudel, Nicole Bachhofner, Christina Kapusta, Peter  ...  Diepolder, Stefan Lüth, Sandra Feyerabend, Maria C. Jung, Magdalena Rogalska-Taranta, Verena Schlaphoff, Markus Cornberg, Michael P.  ... 
doi:10.1002/hep.26034 fatcat:fjs6ma3yuvfexm7cwrnrqnyacq

Index and Queries for the Bibliometric Data of the HICSS 2022 Submission: "Trends in Academic and Industrial Research on BusinessProcess Management - A ComputationalLiterature Analysis" [article]

Fabian Muff, Felix Härer, Hans-Georg Fill
2021 Zenodo  
Appel Technical University of Darmstadt 0 Stefan Bachhofner Vienna University of Business and Economics 0 Stefan Bloemheuvel Tilburg University 0 Stefan Esser Eindhoven University of Technology  ...  Stefan Mutke University of Leipzig 0 Stefan Pottinger IPL Information Processing Ltd 2 Stefan R.  ... 
doi:10.5281/zenodo.5511770 fatcat:uujoqsvetrbaxiaae3qawmf5wq

Acknowledgment to Reviewers of Sensors in 2020

Sensors Editorial Office Sensors Editorial Office
2021 Sensors  
Han, Lei Stefan, Markiewicz Jakub Han, Meng Stefan, Ovidiu Han, Ning Stefańczyk, Maciej  ...  Bacem, Mbarek Mazumder, Abhishek Bachelder, Edward Mazurek, Miroslaw Bachhofner  ... 
doi:10.3390/s21030854 pmid:33525311 fatcat:nstzo7kmhbhabjy72svfsbhhky

A New Approach for Automatic Removal of Movement Artifacts in Near-Infrared Spectroscopy Time Series by Means of Acceleration Data

Andreas Metz, Martin Wolf, Peter Achermann, Felix Scholkmann
2015 Algorithms  
and Stefan Kleiser for the artifact scoring.  ...  The authors would like to thank Fiona Pugin and Reto Huber for the close collaboration with the data collection, Madlaina Stauffer and Urs Bachhofner for help with the data collection and Raphael Zimmermann  ... 
doi:10.3390/a8041052 fatcat:dtleytal65dirl2vtro7x4qqpi