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TU Berlin

Inhalt des Dokuments

Data Science

Lupe

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.

Publications

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.


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


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


Victor, F. and Ruppel, P. and Kuepper, A. (2021). A Taxonomy for Distributed Ledger Analytics. IEEE Computer. IEEE, 30-38.


Victor, F. and Weintraud, A. M. (2021). Detecting and Quantifying Wash Trading on Decentralized Cryptocurrency Exchanges. Proceedings of The Web Conference 2021


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


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)


Romiti, M. and Victor, F. and Moreno-Sanchez, P. and Nordholt, P. S. and Haslhofer, B. and Maffei, M. (2021). Cross-Layer Deanonymization Methods in the Lightning Protocol. Financial Cryptography and Data Security. Springer International Publishing.


Rodriguez Garzon, S. and Arbuzin, D. and Küpper, A. (2017). Geofence Index: A Performance Estimator for the Reliability of Proactive Location-Based Services. 2017 18th IEEE International Conference on Mobile Data Management (MDM), 1-10.


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


Motta, M. and Hagemann, T. and Fischer, S. and Assion, F. (2021). EvolMusic: Towards Musical Adversarial Examples for Black-Box Attacks on Speech-To-Text. 2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21 Companion), 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)


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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