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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

MAIA: A Microservices-based Architecture for Industrial Data Analytics
Citation key Tuan2019
Author Dinh Tuan, H. and Beierle, F. and Rodriguez Garzon, S.
Title of Book IEEE International Conference on Industrial Cyber-Physical Systems (accepted for publication)
Year 2019
Bibtex Type of Publication SNET Cloud Data
Link to publication Download Bibtex entry

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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