Documents

Actions Registration Registration Fees Deadlines and Policies Submit Paper Guidelines Preparing your Presentation Templates Glossary AI Tools Author's Login Reviewer's Login Ethics of Review Information Conference Details Important Dates Technical Program Social Event Call for Papers Program Committee Event Chairs Keynote Lectures Best Paper Awards Satellite Events Workshops Special Sessions Tutorials Demos Panels Travel and Accommodation Conference Venue About the Region Reaching the City Visa Information Hotel Reservation Partners Academic Partners Industrial Partners Institutional Partners Media Partners Partner Events Publication Partners Previous Conferences Websites Abstracts Invited Speakers Awards Books Published

Keynote Lectures

Property Graph Transformations in Action: From Data Integration to Causal Analysis
Angela Bonifati, CNRS LIRIS, Lyon 1 University, France

The Future of Agriculture through Data Science: Challenges and Opportunities for Autonomous Field Systems
João Paulo Papa, Sao Paulo State University, Brazil

Data Management Meets Its AI Partner
Alon Halevy, Google Cloud, United States

Property Graph Transformations in Action: From Data Integration to Causal Analysis

Angela Bonifati
CNRS LIRIS, Lyon 1 University, France

Short Bio
Angela Bonifati is a Distinguished Professor of Computer Science at Lyon 1 University and at the CNRS LIRIS research laboratory, where she leads the Database Group. She is also an Adjunct Professor at the University of Waterloo, Canada since 2020, and a Senior Member of the French University Institute (IUF) since 2023. Her current research interests span several aspects of data management, including graph databases, knowledge graphs, and data integration, as well as their applications to data science and artificial intelligence. She has co-authored numerous publications in top venues in the data management field, including five Best Paper Awards, two books, and an invited paper in ACM SIGMOD Record (2018). She is an ACM Fellow and the recipient of a European Research Council Advanced Grant (2024). Her work has been recognized with the VLDB Women in DB Research Award (2025), the IEEE TCDE Impact Award (2023), and an ACM SIGMOD Research Highlights Award (2023). She is the current Chair of ACM SIGMOD (2025-2029).

Abstract
Property graphs are key components of modern graph databases and graph analytics systems. They support highly expressive data models consisting of multi-labeled nodes and edges, together with properties represented as key-value pairs. Property graphs serve as versatile data integration paradigms, enabling data in virtually any format to be seamlessly transformed into this model. Moreover, they are at the core of an active standardization effort led by ISO/IEC, which aims to establish standardized declarative graph query languages such as GQL and SQL/PGQ. In addition to these data manipulation language standards, complementary languages for property graph schemas and constraints are emerging as part of future data definition languages. In this talk, I will present novel declarative paradigms for expressing property graph transformations that support both graph-based data integration and data cleaning tasks. Beyond being declarative, these transformations are designed to achieve efficiency and scalability. Furthermore, they are sufficiently flexible to be applied in other contexts, such as causal inference and causal analysis, where declarative graph languages enable complex, path-based causal operations.

The Future of Agriculture through Data Science: Challenges and Opportunities for Autonomous Field Systems

João Paulo Papa
Sao Paulo State University, Brazil
https://papajpblog.wordpress.com/

Short Bio
João Paulo Papa holds a BSc in Information Systems from the São Paulo State University (Brazil), MSc in Computer Science from the Federal University of Sao Carlos (Brazil), Ph.D. in Computer Science from the University of Campinas (Brazil), Post-Doctorate at the University of Campinas (Brazil), Harvard University and MIT (USA), He is a Senior IEEE Member and Fellow of the International Association for Pattern Recognition (IAPR), Asia-Pacific Artificial Intelligence Association (AAIA), and a Research Fellow of the Alexander von Humboldt Foundation (Germany).

Abstract
Autonomous weed management represents one of the next frontiers in precision agriculture, yet perennial crops such as sugarcane remain especially challenging due to dense vegetation and the “green-on-green” similarity between crops and weeds. In this talk, we present a realistic in-field benchmark dataset and evaluate modern deep learning approaches for weed detection, classification, and segmentation under real-world agricultural conditions. Our results reveal an important gap between laboratory performance and field readiness: while classification approaches achieved near-perfect accuracy, robust weed detection remains an open challenge. We discuss architectural insights, deployment considerations, and the path toward practical AI-driven systems capable of supporting sustainable and autonomous agriculture.

Data Management Meets Its AI Partner

Alon Halevy
Google Cloud, United States

Short Bio
Alon Halevy is a Distinguished Engineer at Google Cloud, where he works on extending data management with GenAI. From 2019 until November 2023, he was a director at Meta’s Reality Labs Research, where he worked on Personal Digital Data, the combination of neural and symbolic techniques for data management and on Human Value Alignment. Prior to Meta, Alon was the CEO of Megagon Labs (2015-2018) and led the Structured Data Group at Google Research (2005-2015), where the team developed WebTables and Google Fusion Tables. From 1998 to 2005 he was a professor at the University of Washington, where he founded the database group. Alon is a founder of two startups, Nimble Technology and Transformic (acquired by Google in 2005). Alon co-authored three books: The AI Partner (2026), The Infinite Emotions of Coffee and Principles of Data Integration. In 2021 he received the Edgar F. Codd SIGMOD Innovations Award. Alon is a Fellow of the ACM and a recipient of the PECASE award and Sloan Fellowship. Together with his co-authors, he received VLDB 10-year best paper awards for the 2008 paper on WebTables and for the 1996 paper on the Information Manifold data integration system.

Abstract
Generative AI is disrupting data management on multiple levels. It enables us to finally query structured and unstructured data in a uniform fashion, while simultaneously democratizing access to data through natural language interfaces to querying. Beyond that, AI enables us to substantially reimagine the complexity of questions we can ask from collections of data and the scope of decisions that data can help support. In this talk I will discuss how Google Cloud Data is innovating in these areas, as well as share ambitious ideas for us to tackle as a community.