Enabling automated data integration and cleaning has been a fundamental research goal for several decades because the requirements heavily depend on the application scenario. Recent learning-based techniques rely on heavy parameter tuning by experts or the provision of large amounts of labeled data, impeding their deployment in ad-hoc integration workflows. 

In my talk, I discuss how we overcome the aforementioned problems by building systems that follow example-based and declarative paradigms.

Ziawasch Abedjan is Professor for “Databases and Information Systems” at Leibniz Universität Hannover. He is Junior Fellow of the German Computer Science Society, Fellow of the Berlin institute on Foundation of Learning and Data and member of the L3S Research Center. Ziawasch Abedjan received his PhD at the Hasso-Plattner-Institute in Potsdam and received the best dissertation award of the University of Potsdam in 2014. After his PhD, he was a postdoctoral associate at MIT and later Junior Professor at the TU Berlin. Further, he was Senior Researcher at the German Center for Artificial Intelligence (DFKI) and Visiting Academic at Amazon. Ziawasch has published more than 70 peer-reviewed papers in the area of data integration and data analytics and is recipient of the SIGMOD 2019 most reproducible paper award, SIGMOD 2015 best demonstration award, and the CIKM 2014 best student paper award. His research is funded by the German Research Foundation (DFG) and the German Ministry of Research and Education (BMBF).