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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

Hagemann, T. and Katsarou, K. (2020). A Systematic Review on Anomaly Detection for Cloud Computing Environments. 3rd Artificial Intelligence and Cloud Computing Conference (AICCC 2020). ACM, 83-96.


Beierle, F. and Tran, V.T. and Allemand, M. and Neff, P. and Schlee, W. and Probst, T. and Zimmermann, J. and Pryss, R. (2020). 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, 2277–2289.


Beierle, F. and Aizawa, A. and Collins, A. and Beel, J. (2020). Choice Overload and Recommendation Effectiveness in Related-Article Recommendations. Analyzing the Sowiport Digital Library. International Journal on Digital Libraries. Springer, 231–246.


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


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.


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


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