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Conferences / EDBT/ICDT Joint Conference

EDBT/ICDT 2020

Mar 30, 2020 – Apr 2, 2020 · Copenhagen, Denmark Online

Conference
EDBT/ICDT Joint Conference Academic
Dates
Mar 30, 2020 – Apr 2, 2020
Venue
—
Place
Copenhagen, Denmark
Format
Online
Status
Past
Attendance
—
No exact count. Organizers' report (arXiv 2004.07668): post-event survey answered by 114 participants, 'over 42% of the registered participants' (implies roughly 270 registered); the face-to-face event normally attracts '250 to 300 delegates'.
Website
diku-dk.github.io
Recorded
5 sponsors · 4 speaker appearances
Notes
23rd EDBT/ICDT. Planned for Copenhagen (host: DIKU, University of Copenhagen); about three weeks before, the in-person event was cancelled due to COVID-19 and held fully online and synchronous on Zoom. In-person fees below were the original pre-COVID prices; the virtual fee was set at EUR 300 and later lowered to EUR 230 with refunds. Planned in-person venue not confirmed from the conference site. Supporters: University of Copenhagen, Aalborg University, Aarhus University, Carlsberg Foundation, Otto Mønsted Fond. speakers: keynotes only.

Ticket prices

TicketPriceValid untilNotesSource
Student (early, original in-person)€500Feb 5, 2020diku-dk.github.io
Regular (early, original in-person)€650Feb 5, 2020Pre-COVID in-person fee; early deadline extended from Feb 2 to Feb 5diku-dk.github.io
Student (normal, original in-person)€600Mar 1, 2020diku-dk.github.io
Regular (normal, original in-person)€750Mar 1, 2020diku-dk.github.io
Virtual event registration (final)€230—Initially EUR 300; reduced to EUR 230 and excess refundeddiku-dk.github.io
Student (late/onsite, original in-person)€700—diku-dk.github.io
Regular (late/onsite, original in-person)€850—diku-dk.github.io

Sponsors 5

Speakers and talks 4

SpeakerCompanyRoleTalkLinks
Ashwin MachanavajjhalaDuke UniversityKeynoteFair Decision-Making using Privacy Protected Data—
Benny KimelfeldTechnionKeynoteFacets of Probabilistic Databases—
Juan ReutterPontificia Universidad Católica de ChileKeynoteCurrent Challenges in Graph Databases—
Neoklis (Alkis) PolyzotisGoogleKeynoteFrom Data to Models and Back: Experiences from Google's Production ML Pipelines—

Sources