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Data Science


Data in today’s business landscape are created and stored at exponentially large scales. Therefore, the need to improve business operations through data-driven decisions has emerged as an important objective for many growing companies. The field of data science addresses those needs by combining computer science, engineering, mathematics, statistics, and predictive modeling to generate analytical insights about data from a variety of sources.   Data often require a great amount of cleaning and pre-processing; so many research topics are also directed toward identifying different solutions for parallelized and distributed computing and data storage: from the big players in this market like Apache Hadoop and Spark through to CUDA. The field of data science interfaces with a variety of other disciplines, and by utilizing new computational technologies together with statistics and predictive modeling we strive to provide unique analytical insights from data at large scales.

Data Science research projects at SNET currently investigate data from the automotive, energy, and mobile communications domains. As such, we are often involved with the processing and analysis of geospatial data with both structured and unstructured formats.  Our goal is to discover the statistical relationships buried deep within data, and to use that knowledge as the framework for prototype development.


Dinh Tuan, H. and Beierle, F. and Rodriguez Garzon, S. (2019). MAIA: A Microservices-based Architecture for Industrial Data Analytics. IEEE International Conference on Industrial Cyber-Physical Systems (aceepted for publication)

Lorbeer, B. and Deutsch, T. and Ruppel, P. and Küpper, A. (2019). Anomaly Detection with HMM Gauge Likelihood Analysis. BDS2019 accepted for publication.

Eichinger, T. and Beierle, F. and Khan, S. U. and Middelanis, R. and Sekar, V. and Tabibzadeh, S. (2019). affinity: A System for Latent User Similarity Comparison on Texting Data. IEEE-ICC (to appear)

Zielinski, E. and Schulz-Zander, J. and Ruckstuhl, H. and Artemenko, A. and Ramirez, A. and Zeiger, F. and Mormul, M. and Hetzelt, F. and Beierle, F. and Klaus, H. and Zimmermann, M. and Schellenberger, C. (2019). Secure Real-time Communication and Computing Infrastructure for Industry 4.0 – Challenges and Opportunities. Proceedings 2019 Advanced Communication Networks for Industrial Applications (AIComNets). IEEE (to appear).

Victor, F. and Lüders, B. (2019). Measuring Ethereum-based ERC20 Token Networks. International Conference on Financial Cryptography and Data Security, tbd.

Beierle, F. and Aizawa, A. and Collins, A. and Beel, J. (2019). Choice Overload and Recommendation Effectiveness in Related-Article Recommendations. Analyzing the Sowiport Digital Library. International Journal on Digital Libraries. Springer (accepted for publication).

Lüders, B. and Ruppel, P. (2018). Macroscopic Patterns in Sparse Location Data: Identifying Mobility Prototypes. Proceedings of the 4th IEEE International Conference on Big Data Service and Applications (BigDataService 2018)

Rodriguez Garzon, S. and Arbuzin, D. and Küpper, A. (2017). Geofence Index: A Performance Estimator for the Reliability of Proactive Location-based Services. Proceedings of the 18th IEEE International Conference on Mobile Data Management (IEEE MDM 2017). IEEE.

Beierle, F. and Aizawa, A. and Beel, J. (2017). Exploring Choice Overload in Related-Article Recommendations in Digital Libraries. Proceedings of the 5th International Workshop on Bibliometric-enhanced Information Retrieval (BIR2017). CEUR-WS, 51–61.

Lorbeer, B. and Kosareva, A. and Deva, B. and Softić, D. and Ruppel, P. and Küpper, A. (2017). Variations on the Clustering Algorithm BIRCH. Big Data Research. Elsevier.

Eichinger, T. (2017). The Corpus Replication Task. Proceedings of the 2017 International Conference on Computational Science & Computational Intelligence

Deva, B. and Raschke, P and Rodriguez Garzon, S. and Küpper, A. (2017). STEAM: A Platform for Scalable Spatiotemporal Analytics. 8th International Conference on Ambient Systems, Networks and Technologies, ANT 2017, 731-736.

Victor, F. and Rodriguez Garzon, S. and and Küpper, A. (2017). Smartphone-collected Mobile Network Events for Mobility Modeling. 14th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2017)

Eichinger, T. and Winter, S. (2017). Regularly Varying Functions, Generalized contents, and the spectrum of fractal strings. Summer School on Fractal Geometry and Complex Dynamics: In Celebration of the 60th Birthday of M. L. Lapidus

Lorbeer, B. and Kosareva, A. and Deva, B. and Softić, Dvzenan and Ruppel, P. and Küpper, Axel (2016). A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm. A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm. Springer, 169–178.

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