[Newsletter PoDM ] Principles of Data Management, Newsletter 56, December 2025

[newsletter at databasetheory.org]

Principles of Data Management, Newsletter 56, December 2025
The newsletter on Principles of Data Management from databasetheory.org

TABLE OF CONTENTS

Call for Nominations - PODS 2026 Test-of-Time Award
Call for Participation - ICDT 2026
Announcement - ICDT 2026 Test-of-Time Award

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Call for Nominations - PODS 2026 Test-of-Time Award

The PODS 2026 Test-of-Time Award Committee consists of Wang-Chiew Tan, George Fletcher, and Frank Neven.

Please email nominations to Frank Neven at frank.neven at uhasselt.be with the subject line “PODS 2026 ToT Award nomination.”  Include a brief justification. Deadline: January 19, 2026. Nominations are confidential and will be shared only among the committee members.”

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Call for Participation - ICDT 2026

The registration site for ICDT/EDBT 2026 is now open: https://edbticdt2026.github.io/?contents=registration.html
Early registration deadline is Feb 10th, 2026.

24th March - 27th March, 2026 , Tampere, Finland

Keynote speakers:
Reinhard Pichler (TU Vienna), Divesh Srivastava (AT&T), Cristian Riveros (PUC Chile), Pınar Tözün (IT University of Copenhagen), Alon Halevy (Google)

Invited ICDT Lecture:
Florent Capelli (Universite d'Artois)

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ICDT 2026 TEST-OF-TIME AWARD

* The ICDT 2026 Test-of-Time Award will go to
  Vince Barany, Balder ten Cate, Benny Kimelfeld, Dan Olteanu, Zografoula Vagena:
  Declarative Probabilistic Programming with Datalog.

* The paper proposes Generative Datalog, a probabilistic extension of Datalog that
   allows sampling from discrete probability distributions. Generative Datalog can be
   seen as a declarative probabilistic programming language that operates on standard
   relational databases.
   The idea is simple but elegant: Given that we can view an existential Datalog
   program as a generator of families of models, why not turn it into a generator of a
   probabilistic model? On the side of language design, essentially all it takes is to
   attach probability distributions to the tuple-generating dependencies. The easy
   concept of the language is paired with a surprisingly deep mathematical background:
   Even though all distributions discussed in this initial paper are discrete, laying the
   semantic groundwork already requires excursions into measure theory.
   The paper explains the language, defines the semantics, a probabilistic version of the
   chase, discusses adding constraints in the spirit of probabilistic programming, and
   touches upon the equivalence problem for programs. It generated a significant amount
   of follow-up in a variety of top venues spanning database theory, database systems,
   and programming languages.

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