Boris Otto

Boris Otto is Executive Director at the Fraunhofer Institute for Software and Systems Engineering ISST in Dortmund and holds the Chair of Industrial Information Management at TU Dortmund University. His research focus is on Corporate Data Management, Data Sovereignty for Data Ecosystems and Digital Business Engineering. He obtained his doctorate degree (Dr.-Ing) at the University of Stuttgart under supervision of Prof. Dr. Hans-Jörg Bullinger, former president of the Fraunhofer-Gesellschaft. He received his post-doctoral qualification (habilitation) from the School of Management at the University of St. Gallen at the chair of Prof. Dr. Hubert Österle, where he founded and headed the Competence Center Corporate Data Quality. His professional career also comprises various responsibilities at PricewaterhouseCoopers, SAP and Fraunhofer Institute for Industrial Engineering IAO. In addition, Boris Otto was a research fellow at the Center for Digital Strategies at the Tuck School of Business at Dartmouth College, New Hampshire, USA. He is founder and Member of the Advisory Board of CDQ AG, a St. Gallen start-up focusing on data sharing services. Furthermore, Boris Otto is Vice President of the Board of Directors of the International Data Spaces (IDS) Association.

Anne-Marie Kermarrec

Anne-Marie Kermarrec is Professor at EPFL (Switzerland) since January 2020. Before that she was the CEO of the Mediego startup that she founded in April 2015. Mediego provides content personalisation services for online publishers. She was a research director at Inria, France from 2004 to 2015. She got a PhD thesis from University of Rennes (France), and has been with Vrije Universiteit, NL and Microsoft Research Cambridge, UK. Anne-Marie received an ERC grant in 2008 and an ERC proof of Concept in 2013. She received the Montpetit Award in 2011 and the Innovation Award in 2017 from the French Academy of Science. She has been elected to the European Academy in 2013 and named ACM Fellow in 2016. Her research interests are large-scale distributed systems, peer to peer networks and system support for machine learning.

Abstract: Recommending Fast Data

Computing systems that make human sense of big data, usually called personalization systems or recommenders, and popularized by Amazon and Netflix, essentially help Internet users extracting information of interest to them. Leveraging machine learning techniques, research on personalization has mainly focused on improving the quality of the information extracted. Yet, building an operational recommender goes far beyond, especially in a world where data is not only big but also changes very fast. This talk will discuss system challenges to scale to a large number of users and a growing volume of fastly changing data to eventually provide real-time personalization.

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