SIGMOD 2023: Keynote Talks
Keynote Speaker 1: Don Chamberlin, IBM Fellow (retired)
49 Years of Queries
Abstract
The relational data model, proposed by Ted Codd in the 1970s, has been the dominant paradigm for storing and accessing business data for several decades. In this talk, I’ll share some stories from the early days of relational databases, and examine some reasons for the remarkable resilience of relational database technology. I’ll discuss some of the challenges to the relational approach that have arisen over the years. I’ll also discuss the evolution of SQL, and offer some thoughts about how the language may continue to evolve in the future.
Bio
Don Chamberlin is best known as co-inventor (with Ray Boyce) of the SQL database
language, and as one of the designers of the XQuery language for querying XML data sources.
Don holds a B.S. degree from Harvey Mudd College and a Ph.D. from Stanford University. He is
a Fellow of ACM and the Computer History Museum, and a recipient of the ACM Software
System Award and the SIGMOD E.F. Codd Innovations Award. For several years, Don
contributed problems and served as a judge at the annual ACM International Collegiate
Programming Contest. He has also served as an adjunct professor of computer science at
University of California, Santa Cruz, and at Santa Clara University.
Keynote Speaker 2: Vanessa Murdock, Amazon
Mixed Methods Machine Learning
Abstract
We propose a new approach to developing machine learned systems, that employs mixed-methods research to understand what to build, and how to make it satisfying and helpful for the customer. The Mixed Methods Machine Learning (MXML) paradigm, starts with a user study, to understand how people behave in an everyday setting (such as shopping for groceries in a grocery store), and to identify points of friction that can be automated, or experiences that can be made more enjoyable. The study observations are mapped to interactions recorded in the system’s behavioral log data, which is the basis for the machine learned system. Mapping the study observations to the log data is a key step in directing the machine learning to solve a customer problem. The MXML system is evaluated with a follow-on user study, in addition to the traditional online A/B test, to assess whether the system is satisfying, helpful and delightful. In this talk we present the MXML paradigm, with real-world examples.
Bio
Vanessa Murdock manages a science team in Alexa Shopping at Amazon, partnering with Amazon’s Choice, Alexa Shopping List and others. She is Chair of the ACM Special Interest Group on Information Retrieval (SIGIR) and is serving as an Editor-In-Chief of the Journal of Information Retrieval. Her research spans a wide range of information retrieval and recommender systems topics, focusing on connecting people’s online and offline lives. Vanessa has her PhD in computer science from the University of Massachusetts. She began her professional life as a classical pianist.
DEI Keynote Speaker: Shazia Sadiq, The University of Queensland
DEI Perspectives in Information Technology Education
Abstract
Information Technology (IT) has become deeply intertwined with business and society across many sectors such as health, transport, manufacturing, and education is no exception. In this talk I will outline some of the challenges in education resulting from increasingly diverse student populations and shifts in delivery modes for learning. I will also share experiences and strategies for embedding DEI perspectives in education of, for and with Information Technology.
Bio
Shazia Sadiq is a Professor of Computer Science at the School of Electrical Engineering and Computer Science, The University of Queensland (UQ). Her research focusses on responsible data management and aims to reduce the socio-technical barriers to data driven transformation, by assisting organisations to create, protect and sustain agile data pipelines. Shazia has been a devoted lecturer for two decades and is an advocate of learning analytics for improving personalized learning and graduate outcomes. In 2012, she received an institutional award for teaching excellence and in 2016 she spearheaded the successful Master of Data Science program at UQ, that is helping overcome skill shortages in Queensland for qualified data scientists. Shazia is currently leading UQ’s flagship Artificial Intelligence Collaboratory, that brings together cross-disciplinary researchers on core, applied and societal aspects of AI. She is the Director for the ARC Centre for Information Resilience 2020-2025, Fellow of the Australian Academy of Technological Sciences and Engineering, Chair of the National Committee on Information and Communication Sciences at the Australian Academy of Science 2019-2023, and member of The Australian Research Council (ARC) College of Experts 2018-2021.
