SIGMOD 2024: Panel Discussions
1) The Future of Graph Analytics
In the last two decades, we have been witnessing high demand for graph-based technologies in industry. On the research side, several recent advances have been made about large-scale graph processing, graph analytical systems and graph databases. The landscape of graph query languages is currently evolving with the definition of new standards, and the need for domain-specific languages to express graph algorithmic and analytical primitives will continue and increase in the next future. In this SIGMOD panel, we will discuss the impact of the above changes on the future of graph analytics. Is there a demand for more expressive languages and libraries for analyzing relationships in a graph? Are new hybrid OLTP/OLAP architectures required with improved performance and scalability? What are the graph analytical workloads and benchmarks that users expect on real-world graph applications? What will be the impact of graph ML on graph analytical systems? How to adapt these systems to the dynamic changes that are ubiquitous for graph-shaped data? These and other questions will be addressed in the panel.
Panel organizer:
Angela Bonifati, Lyon 1 University, CNRS & IUF
Moderator:
Angela Bonifati, Lyon 1 University, CNRS & IUF
Panelists:
GM. Tamer Özsu, University of Waterloo Yuanyuan Tian, Microsoft Gray Systems Lab Hannes Voigt, Neo4j Wenyuan Yu, Alibaba Group Wenjie Zhang, University of New South Wales
2) AI for Systems
In the last decade, data management has been steadily moving towards the Cloud(s), turning DBMS “builders” into “operators”. The combined scale and vertical integration afforded us with the opportunity to scale/optimize systems based on a telemetry feedback loop. These events perfectly aligned with the fast advances in the field of Data Science creating the perfect conditions for an explosion of “ML for Systems” solutions. More recently the massive improvements in capabilities for foundation models has introduced a new spicy ingredient into the mix. In this panel, we explore questions around the practical adoption and robustness of ML for Systems solutions, as well as the potential role for Large Language Models (LLMs) and other foundation models, spanning very concrete engineering discussions/thinking and very open-ended “future of this industry” type thinking.
Panel organizers:
Carlo Curino, Microsoft Raghu Ramakrishnan, Microsoft
Moderator:
Raghu Ramakrishnan, Microsoft
Panelists:
Tim Kraska, MIT & Amazon Web Services Yuanyuan Tian, Microsoft Bolin Ding, Alibaba Fatma Ozcan, Google
