Exploratory Data Analysis (EDA) is an iterative and tedious process. Several strategies have been proposed to ease the burden on users in EDA ranging from stepwise to full-guidance approaches. Stepwise approaches rely on computing utility functions that determine the best action to take at each step. Full-guidance approaches rely on learning end-to-end exploration policies. Today’s big question is how to commodify EDA and make it easily deployable for all but for that we need to know what users are looking for: are they looking for a needle in a haystack, taking a tour of the data, or are they feeling lucky? This talk will investigate those questions and discuss the challenges of storing learned pathways through data or regenerating them when needed.
Sihem Amer-Yahia is a Silver Medal CNRS Research Director and Deputy Director of the Lab of Informatics of Grenoble. She works on exploratory data analysis and fairness in job marketplaces. Before joining CNRS, she was Principal Scientist at QCRI, Senior Scientist at Yahoo! Research and Member of Technical Staff at at&t Labs. Sihem is PC chair for SIGMOD 2023 and vice president of the VLDB Endowment. She currently leads the Diversity, Equity and Inclusion initiative for the database community.
All are welcome!