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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

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


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.


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


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.


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.


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.


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 . International Journal of Statistics & Applications. Scientific & Academic Publishing Company, 160-169.


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