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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

Cross-Layer Deanonymization Methods in the Lightning Protocol
Citation key romiti2021crosslayer
Author Romiti, M. and Victor, F. and Moreno-Sanchez, P. and Nordholt, P. S. and Haslhofer, B. and Maffei, M.
Title of Book Financial Cryptography and Data Security
Year 2021
Address Cham
Editor Borisov, Nikita. and Diaz, Claudia
Publisher Springer International Publishing
Bibtex Type of Publication SNET Data Blockchain
Link to publication Download Bibtex entry

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