ACM SIGMOD Seattle, USA, 2023
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SIGMOD 2023: Panel Discussions

1) Future of Database System Architectures

Major technology disruptions on multiple fronts—HW/SW disaggregation, cloud infrastructure, impact of ML in the DB software lifecycle, novel data workloads, etc.—have prompted broad rethinking of traditional database architectures. With the demise of Moore’s Law, we are seeing a lot of interest (and start-ups with significant investments) in hardware database accelerators, exploring FPGAs, GPUs, and more. How will these impact DB system design, how will DB systems look like in the near future? Where are the hardest research challenges? What learnings from the past will guide us through these disruptions?


Raghu Ramakrishnan, Microsoft


Gustavo Alonso, ETH
Natassa Ailamaki, EPFL
Sailesh Krishnamurthy, Google
Sam Madden, MIT
Swami Sivasubramanian, AWS

2) Personal Data for Personal Use: Vision or Reality?

The vision of collecting all of one’s personal information into one searchable database has been around at least since Vannevar Bush’s 1945 paper on the Memex System. In the late 1990’s, Gordon Bell and his colleagues at Microsoft Research built MyLifeBits, which was the first serious attempt to build such a database. Since then, technology has improved on several dimensions, making the vision much more realistic. Disks have become much cheaper and therefore storing all of the information is affordable. Mobile technology has significantly enhanced our ability to capture the experiences in our lives, and in practice we’re already leaving digital breadcrumbs about our activities. With the advent of smart glasses, we will be able to easily capture everything we see and experience with little friction. Finally, AI has advanced to the point where it’s possible to create higher-level summaries from individual pieces of raw data (e.g., photos, our movements on maps, shopping records). This panel will discuss the issues that separate the vision from current reality and focus our attention on the challenges that need to be overcome.


Alon Halevy, Meta
Wang-Chiew Tan, Meta


Xin Luna Dong, Meta
Bo Li, University of Illinois
Julia Stoyanovich, NYU
Anthony Kum Hoe Tung, University of Singapore
Gerhard Weikum, MPI

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