Prof. Dr. Christoph Lofi, TU Delft, The Netherlands
Date: Thursday, October 28, 2021, 17:15 h
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With the growing success of AI systems and other data-driven applications, the need for obtaining relevant and reliable data for these systems becomes a central bottleneck. However, while wrangling and shaping data for such systems usually takes the majority of system development efforts, the resulting Data Engineering Pipelines are rarely in the spotlight. Also, modern Data Engineering Pipelines increasingly need to become more powerful, and have to also tackle complex semantic challenges beyond the technical problem of just connecting different systems with different formats. In this talk, we highlight some of these semantic challenges in data engineering and their relevance, and discuss some approaches and solutions to them.
Prof. Dr. Christoph Lofi has been an Assistant Professor at the Web Information Systems group of the Faculty of Engineering, Mathematics and Computer Science (EEMCS/EWI), Delft University of Technology, since 2016. His research is on Semantic Data Engineering methods and techniques. The general problem is that the source data available to a data-driven system is often not fit for that purpose because data is scattered between different sources, is of low quality, or important semantic information is only implicitly available. This is a central challenge in the arising data-driven economy and a major detrimental aspect in many AI- or Data Science-driven systems. Semantic Data processing pipelines are needed to overcome these issues, producing the required target data from the available source data.
Christoph received his PhD from TU Braunschweig, Germany, in 2011 in the field of Data Management and Query processing, was a Research Fellow at National Institute of Informatics Tokyo, Japan, from 2012-2014, and spent several years as a research fellow at L3S Research Centre in Hannover, Germany.