direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Inhalt des Dokuments

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.


Lorbeer, B. and Deutsch, T. and Ruppel, P. and Küpper, A. (2019). Anomaly Detection with HMM Gauge Likelihood Analysis. 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), 1–8.

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.

Victor, F. and Lüders, B. (2019). Measuring Ethereum-Based ERC20 Token Networks. Financial Cryptography and Data Security. Springer International Publishing, 113–129.

Eichinger, T. and Winter, S. (2019). Regularly varying functions, generalized contents, and the spectrum of fractal strings. Contemporary Mathematics. American Mathematical Society, 63-94.

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.

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, 20–28.

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, 68-75.

Grunert, K. (2020). Overview of JavaScript Engines for Resource-Constrained Microcontrollers. 5th International Conference on Smart and Sustainable Technologies 2020(SpliTech 2020), 1-7.

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.

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.

Dinh Tuan, H. and Mora Martinez, M. and Beierle, F. and Rodriguez Garzon, S. (2020). Development Frameworks for Microservice-based Applications: Evaluation and Comparison (accepted for publication. 2020 European Symposium on Software Engineering (ESSE)

Rodriguez Garzon, S. and Louis, B. (2020). Context Flow Graphs: Situation Modeling for Rule-based Proactive Context-aware Systems. IEEE Access, 1-22.

Zusatzinformationen / Extras

Quick Access:

Schnellnavigation zur Seite über Nummerneingabe

TU Berlin - Service-centric Networking - TEL 19
Ernst-Reuter-Platz 7
10587 Berlin, Germany
Phone: +49 30 8353 58811
Fax: +49 30 8353 58409