From newsletter at databasetheory.org Mon Dec 8 16:09:18 2025 From: newsletter at databasetheory.org (newsletter at databasetheory.org) Date: Mon, 8 Dec 2025 16:09:18 +0000 Subject: [Newsletter PoDM ] Principles of Data Management, Newsletter 56, December 2025 In-Reply-To: References: Message-ID: 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 --------------------------------------------------------------------- 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.? --------------------------------------------------------------------- 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), P?nar T?z?n (IT University of Copenhagen), Alon Halevy (Google) Invited ICDT Lecture: Florent Capelli (Universite d'Artois) --------------------------------------------------------------------- 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. --------------------------------------------------------------------- The next issue of this newsletter is scheduled for early January 2026. Please submit your announcements to newsletter-owner at databasetheory.org until December 31, 2025. Please follow the formatting instructions at databasetheory.org/newsletter. Past issues of the newsletter can be found at databasetheory.org/newsletter.