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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

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


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.


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)


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).


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)


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.


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


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. IEEE, 23–30.


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


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.


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.


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. Proc. 2019 IEEE International Conference on Communications (ICC). IEEE, 1–7.


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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