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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

DEEPFLOW: Toawards Network-Wide Ingress Traffic Prediction Using Machine Learning At Large Scale
Citation key KatsarouISNCC2020
Author Fischer, S. and Katsarou, K. and Holschke, O.
Title of Book 7th International Symposium on Networks, Computers and Communications(ISNCC 2020)
Year 2020
Publisher IEEE
Bibtex Type of Publication SNET Data Science
Link to publication Download Bibtex entry

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