VLDB 2022: Keynote Speakers
Big Graphs: Challenges and Opportunities (University of Edinburgh, Shenzhen Institute of Computing Sciences) Abstract: Graphs are not new in database community – we have long used them to model and reason about database operations and some of our earliest systems were built on graph models. There has been a renewed interest in the last decade in using graphs model real-life phenomena in many domains. Recent studies suggest that there is interest among the user community in using graphs to model applications and data that have traditionally been considered the domain of relational systems. Despite intense research and development efforts in graph processing, these efforts are fragmented, and general purpose, scalable solutions are not yet available. In this talk, I will provide a systematic look at the field and highlight some of the open research issues. Bio: Professor Wenfei Fan is the Chair of Web Data Management at the University of Edinburgh, UK, and the Chief Scientist of Shenzhen Institute of Computing Science, China. He is a Fellow of the Royal Society (FRS), a Fellow of the Royal Society of Edinburgh (FRSE), a Member of Academia Europaea (MAE), an ACM Fellow (FACM), and a Foreign Member of Chinese Academy of Sciences. He received his PhD from the University of Pennsylvania, and his MSc and BSc from Peking University. He is a recipient of Royal Society Wolfson Research Merit Award in 2018, ERC Advanced Grant in 2015, the Roger Needham Award in 2008 (UK), Yangtze River Scholar in 2007 (China), the Outstanding Overseas Young Scholar Award in 2003 (China), the Career Award in 2001 (USA), and several Test-of-Time and Best Paper Awards (Alberto O. Mendelzon Test-of-Time Award of ACM PODS 2015 and 2010, Best Paper Awards for SIGMOD 2017, VLDB 2010, ICDE 2007 and Computer Networks 2002). His current research interests include database theory and systems, in particular big data, data quality, data sharing, parallel computation, query languages and recommender systems.
AI-Powered Data-Driven Education (CNRS, Univ. Grenoble Alpes, France) Abstract: Educational platforms are increasingly becoming AI-driven. Besides providing a wide range of course filtering options, personalized recommendations of learning material and teachers are driving today's research. While accuracy plays a major role in evaluating those recommendations, many factors must be considered including learner retention, throughput, upskilling ability, equality of learning opportunities, and satisfaction. This creates a tension between learner-centered and platform-centered approaches. I will describe research at the intersection of data-driven recommendations and education theory. This includes multi-objective algorithms that leverage collaboration and affinity in peer learning, studying the impact of learning strategies on platforms and people, and automating the generation of sequences of courses. I will end the talk with a discussion of the central role data management systems could play in enabling holistic educational experiences. Bio: 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&Inclusion initiative for the database community.
Data to the People? Reflections on Trying to Help People Struggle Less with Data (University of Washington) Abstract: For over a decade, my collaborators and I have sought to better understand how people work with data, characterize some of the challenges involved, and develop new interactive systems to help people achieve their goals. These efforts have led to popular languages for interactive data visualization, visualization recommender systems, interactive tools for data cleaning and transformation at scale, and novel approaches to statistical modeling and effective error analysis. In this talk I will share reflections gathered from these projects, including the immense value of human-computer interaction to data management (and vice-versa), the unreasonable effectiveness of declarative domain-specific languages, and the judicious use of AI/ML methods to bridge gaps between user intent and actual data workflows. In addition to what I think we got right, I will consider how our visions of a data-driven world have at times been all-too-rosy, and try to draw corresponding implications for our communities' future. Bio: Jeffrey Heer is the Jerre D. Noe Endowed Professor of Computer Science & Engineering at the University of Washington, where he directs the Interactive Data Lab and conducts research on data visualization, human-computer interaction and social computing. The visualization tools developed by Jeff and his collaborators – Vega(-Lite), D3.js, Protovis, Prefuse – are used by researchers, companies, and data enthusiasts around the world. Jeff's research papers have received awards at the premier venues in Human-Computer Interaction and Visualization (ACM CHI, UIST, CSCW, IEEE InfoVis, IEEE VAST, EuroVis). Honors include MIT Technology Review's TR35 (2009), a Sloan Fellowship (2012), the ACM Grace Murray Hopper Award (2016), the IEEE Visualization Technical Achievement Award (2017), and induction into IEEE Visualization (2019) and ACM SIGCHI academies (2021). Jeff received B.S., M.S., and Ph.D. degrees in Computer Science from UC Berkeley, whom he then "betrayed" to join the Stanford CS faculty (2009–2013). He also co-founded Trifacta, a provider of interactive tools for scalable data transformation acquired by Alteryx in 2022.