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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.


Beierle, F. and Probst, T. and Allemand, M. and Zimmermann, J. and Pryss, R. and Neff, P. and Schlee, W. and Stieger, S. and Budimir, S. (2020). Frequency and duration of daily smartphone usage in relation to personality traits. Digital Psychology. facultas.

Katsarou, K. and Ounoughi, C. and Mouakher, A. and Nicolle, C. (2020). STCMS: A Smart Thermal Comfort Monitor For Senior People. 28th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2020). IEEE (accepted for publication).

Hagemann, T. and Katsarou, K. (2020). Reconstruction-based anomaly detection for the cloud: A comparison on the Yahoo!Webscope S5 dataset. 4th International Conference on Cloud and Big Data Computing(ICCBDC 2020). ACM (accepted for publication).

Grunert, K. (2020). Overview of JavaScript Engines for Resource-Constrained Microcontrollers. 5th International Conference on Smart and Sustainable Technologies 2020(SpliTech 2020)

Fischer, S. and Katsarou, K. and Holschke, O. (2020). DEEPFLOW: Toawards Network-Wide Ingress Traffic Prediction Using Machine Learning At Large Scale. 7th International Symposium on Networks, Computers and Communications(ISNCC 2020). IEEE (accepted for publication).

Victor, F. (2020). Address Clustering Heuristics for Ethereum. Financial Cryptography and Data Security. Springer International Publishing, 617–633.

Müller, M. and Ostern, N. and Rosemann, M. (2020). Silver Bullet for all Trust Issues? Blockchain-based Trust Patterns for Collaborative Business Processes. 18th Int. Conference on Business Process Management (BPM 2020)

Müller, M. and S. Rodriguez Garzon. S. and M. Rosemann, M. and Küpper, A. (2020). Towards Trust-aware Collaborative Business Processes: An Approach to Identify Uncertainty. IEEE Internet Computing, 1-1.

Victor, F. and Hagemann, T. (2019). Cryptocurrency Pump and Dump Schemes: Quantification and Detection. 2019 IEEE International Conference on Data Mining Workshops (ICDMW), 244–251.

Hatzinikolaou D., Katsarou K. (2019). An Account of Principal Components Analysis and Some Cautions on Using the Correct Formulas and the Correct Procedures in SPSS . International Journal of Statistics & Applications. Scientific & Academic Publishing Company, 160-169.

Katsarou, K. and Shekhawat, D. S. (2019). CRD-SentEnse: Cross-domain Sentiment Analysis using an Ensemble Model. Proceedings of The 11th International ACM Conference on Management of Digital EcoSystems(MEDES 2019). ACM (accepted for publication).

Eichinger, T. and Beierle, F. and Papke, R. and Rebscher, L. and Tran, H. and Trzeciak, M. (2019). On Gossip-based Information Dissemination in Pervasive Recommender Systems. ACM RecSys 2019. ACM (in press).

Beierle, F. and Tran, V.T. and Allemand, M. and Neff, P. and Schlee, W. and Probst, T. and Zimmermann, J. and Pryss, R. (2019). What data are smartphone users willing to share with researchers? Designing and evaluating a privacy model for mobile data collection apps. Journal of Ambient Intelligence and Humanized Computing. Springer.

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 (accepted 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.


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