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Stiftungsprofessur Semantische Informationssysteme gefördert von der ROSEN Gruppe.

Semantic Information Systems

Contact

Research Group Semantic Information Systems
Prof. Dr. Martin Atzmüller

Secretary: Jantje Apfeld
sekretariat@informatik.uni-osnabrueck.de
+49 541 969 2480

Semantic Information Systems
Institute of Computer Science
Osnabrueck University
P.O. Box 4469
49069 Osnabrueck, Germany

News

Paper accepted at the 34th International FLAIRS Conference.
Leonid Schwenke and Martin Atzmueller (2021) Show Me What You’re Looking For: Visualizing Abstracted Transformer Attention for Enhancing Their Local Interpretability on Time Series Data.
New Paper in Data Mining and Knowledge Discovery.
Martin Atzmueller, Stephan Günnemann, Albrecht Zimmermann (2021) Mining Communities and their Descriptions on Attributed Graphs: A Survey
New Paper in Expert Systems
Carolina Centeio Jorge, Martin Atzmueller, Behzad M. Heravi, Jenny L. Gibson, Rosaldo J. F. Rossetti, Cláudio Rebelo de Sá (2021) "Want to come play with me?" Outlier Subgroup Discovery on Spatio-Temporal Interactions.
New Paper in IEEE Transactions on Network Science and Engineering.
Cicek Güven, Dietmar Seipel, and Martin Atzmueller (2020) Applying ASP for Knowledge-Based Link Prediction with Explanation Generation in Feature Rich Networks
New Paper in Proc. Ninth International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2020).
Stefan Bloemheuvel, Jurgen van den Hoogen and Martin Atzmueller (2020) Graph Signal Processing on Complex Networks for Structural Health Monitoring
New Paper in Proc. 20th IEEE International Conference on Data Mining - Workshop: Large-scale Industrial Time Series Analysis
Jurgen van den Hoogen, Stefan Bloemheuvel and Martin Atzmueller (2020) An Improved Wide-Kernel CNN for Classifying Multivariate Signals in Fault Diagnosis

SIS Research & Mission

The research of the ROSEN-Group-Endowed Chair of Semantic Information Systems and the according research group, headed by Prof. Dr. Martin Atzmueller, centers around Artificial Intelligence and Data Science. Its major focus is on machine learning and data mining on complex data such as graphs, networks, and temporal data, also with a human-centered perspective.

Overall, our work focuses on how to 'make sense' of complex information and knowledge processes - leveraging the massive amounts of data collected in science and industry by intelligent analytics and semantic interpretation. For instance, this includes the identification of interesting/exceptional patterns and structures, predictive modeling, analysis and exploration of complex heterogeneous and multi-modal data, as well as human-centered decision support.

By connecting computational approaches with the human cognitive, behavioral, and social contextual perspectives - thus linking technologies with their users - our goal is to augment human intelligence and to assist human actors in all their purposes, both online and in the physical world.

People's Info

Prof. Dr. Martin Atzmueller
Head of Semantic Information Systems group.
Research: Artificial Intelligence, Human-Centered Data Science, Knowledge Discovery, Complex Networks, Machine Learning

Research Assistants/PhD Students/External PhD Students

	Arnab Ghosh Chowdhury Deep Learning, Information Engineering, Multi-Modal Learning, Document Intelligence
	Dan Hudson Anomaly and Exceptionality Detection, Deep Capsule Networks, Time Series Analysis, Sensor Data
	Leonid Schwenke
	Stefan Bloemheuvel (JADS): Graph Signal Processing, Graph Neural Networks, Time Series Analysis, Sensor Networks
	Timo Markert (Wittenstein SE): Machine Learning, Sensor Data Analysis, Tactile Object Recognition, Robotic Manipulation
	Parisa Shayan (TiU): Educational Data Mining, Network Analysis, User Modeling, Learning Management Systems
	Jurgen van den Hoogen (JADS): Deep Learning, Time Series Analysis/Classification, Fault Diagnosis, Sensor Data

Projects

MODUS is a project funded by DFG for Model-based Anomaly Pattern Detection and Analysis in Ubiquitous and Social Interaction Networks.
Di-Plast: Digital Circular Economy for the Plastics Industry (funded by Interreg NWE). Di-Plast improves processes for a more stable rPM material supply and quality using artificial intelligence methods and data science approaches: sensoring generates data within supply chains; data analytics provides information about rPM quality, amounts, and supply timing; Value Stream Management improves rPM processes & logistics, environmental assessments validate sustainability.
NWO KIEM ICT ODYN: Observing Team Dynamics and Communication using Sensor-Based Social Analytics.
Resilient Athletes: In this interdisciplinary project (funded by ZonMW), a multidisciplinary personalized human-sensor-based data science approach is being developed and applied. We focus on the resilience of athletes, with the aim that athletes can cope with the physical and mental stress factors to which they are exposed.

Software/Tools

VIKAMINE is an extensible open-source rich-client environment and platform for pattern mining and analytics. VIKAMINE features several powerful and intuitive visualizations complemented by fast automatic mining methods; it is provided as Open Source, under the GNU Lesser General Public License (LGPL).
The R subgroup package (rsubgroup R package) provides a wrapper around the VIKAMINE core.