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Data Science

Lupe [1]
© Service-centric Networking

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.

Team
  • Tobias Eichinger [2]
  • Katerina Katsarou [3]
  • Boris Lorbeer [4]
  • Bianca Lüders [5]
  • Philip Raschke [6]
  • Dr. [7]  [8]Sandro Rodriguez Garzon [9]
  • Friedhelm Victor [10]
  • Tanja Hagemann
    [11]

Projects

  • eBiz [12]
  • Indoor Analytics [13]
  • SPECIAL [14]
  • STEAM [15]
  • Street Smart Retail [16]

Publications

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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 [22]. Digital Psychology. facultas.

Katsarou, K. and Ounoughi, C. and Mouakher, A. and Nicolle, C. (2020). TComf-Predict: A New Tthermal Comfort Prediction Fframework For Elderly People [23]. 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 [24]. 4th International Conference on Cloud and Big Data Computing(ICCBDC 2020). ACM (accepted for publication).

Victor, F. and Hagemann, T. (2019). Cryptocurrency Pump and Dump Schemes: Quantification and Detection [25]. 2019 IEEE International Conference on Data Mining Workshops (ICDMW), tbd.

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 [26]. 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 [27]. 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 [28]. 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 [29]. 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 [30]. 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 [31]. BDS2019 accepted for publication.

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 [32]. IEEE-ICC (to appear)

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 [33]. Proceedings 2019 Advanced Communication Networks for Industrial Applications (AIComNets). IEEE (to appear).

Victor, F. and Lüders, B. (2019). Measuring Ethereum-Based ERC20 Token Networks [34]. Financial Cryptography and Data Security. Springer International Publishing, 113–129.

Beierle, F. and Aizawa, A. and Collins, A. and Beel, J. (2019). Choice Overload and Recommendation Effectiveness in Related-Article Recommendations. Analyzing the Sowiport Digital Library [35]. International Journal on Digital Libraries. Springer.

Eichinger, T. and Winter, S. (2019). Regularly varying functions, generalized contents, and the spectrum of fractal strings [36]. Contemporary Mathematics. American Mathematical Society, 63-94.

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