Conferences / ACM SIGMOD/PODS (International Conference on Management of Data)
SIGMOD/PODS 2020
Jun 14–19, 2020 · Portland, OR, United States Online
- Conference
- ACM SIGMOD/PODS (International Conference on Management of Data) Academic
- Dates
- Jun 14, 2020 – Jun 19, 2020
- Venue
- —
- Place
- Portland, OR, United States
- Format
- Online
- Status
- Past
- Attendance
- 2,950Organizers' experience report: total registration ~2,950 (capped near 3,000 by Zoom limits); 1,330 joined Slack. Online-only; most registrations were free.
- Website
- sigmod2020.org
- Recorded
- 16 sponsors · 3 speaker appearances
- Notes
- Planned for the DoubleTree by Hilton Portland; moved fully online because of COVID-19. speakers: keynotes only. Online fee model per experience report: $300 per regular author registration, $100 workshop-only author, all other participants free.
Ticket prices
| Ticket | Price | Valid until | Notes | Source |
|---|---|---|---|---|
| Non-author online attendee | $0 | — | sigmod2020.org | |
| Author registration (online, workshop-only) | $100 | — | sigmod2020.org | |
| Author registration (online, regular paper) | $300 | — | sigmod2020.org |
Sponsors 16
Title / headline 3
- Megagon Labs · Diamond Sponsors
- Meta · Diamond Sponsors
- Microsoft · Diamond Sponsors
Platinum 11
- Alibaba Cloud · Platinum Sponsors
- Amazon Web Services · Platinum Sponsors
- Futurewei Technologies · Platinum Sponsors
- Google · Platinum Sponsors
- IBM · Platinum Sponsors
- Intel · Platinum Sponsors
- MongoDB · Platinum Sponsors
- Neo4j · Platinum Sponsors
- Oracle · Platinum Sponsors
- Salesforce · Platinum Sponsors
- Snowflake · Platinum Sponsors
Gold 1
- Undo · Gold Sponsor
Media 1
- Now Publishers · Publisher Gold Sponsor
Speakers and talks 3
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Awez Syed | Databricks | Keynote | The Challenge of Building Effective Data Lakes | — |
| Ion Stoica | UC Berkeley | Keynote | Systems and ML: When the Sum is Greater than Its Parts | — |
| Natasha Noy | Keynote | When the Web is your Data Lake: Creating a Search Engine for Datasets on the Web | — |