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2026-09-10 - VLDB26

We were involved in several workshop [1][2], demo [3][4] and research papers [5] at VLDB. This was the first conference attendance and presentation for Rubab and the first conference for Vinny.

TaDA!

Rubab her work on transformation search at https://tabular-data-analysis.github.io/tada2026/ workshop [1]. Initial results demonstrated several orders-of-magnitude speed-up for the AutoJoin system which discovers transformations to join two input columns.

Runtime improvements for AutoJoin

Rubab TaDA-1 Rubab TaDA-2 Rubab TaDA-3

Demos

Our two demos ended up side-by-side.

Boris and Nils Demos

NOVAS

Nils presenting his vision of just-in-time model replacement at https://www.novasworkshop.org/

Nils workshop

  1. Q-ACER: Query Aggregate Constraint Efficient Repair System
    Vaishnavi Deshpande, Seokki Lee, Shatha Algarni, Boris Glavic and Adriane Chapman
    Proceedings of the VLDB Endowment (Demonstration Track). 19, 12 (2026) , 4802–4805.
    details
  2. Speeding Up Transformation Search with Lightweight Statistics
    Rubab Zahra Sarfraz and Boris Glavic
    VLDB 2026 Workshop: Tabular Data Analysis Workshop (TaDA) (2026).
    details
  3. Poodle: Seamlessly Scaling Down Large Language Models with Just-in-Time Model Replacement
    Nils Strassenburg, Boris Glavic and Tilmann Rabl
    VLDB 2026 Workshop: Novel Optimizations for Visionary AI Systems (NOVAS) (2026).
    details
  4. Exploring the Benefits of Just-in-time Model Replacement
    Nils Strassenburg, Boris Glavic and Tilmann Rabl
    Proceedings of the VLDB Endowment (Demonstration Track). 19, 12 (2026) , 4674–4677.
    details
  5. Efficient Query Repair for Aggregate Constraints
    Shatha Algarni, Boris Glavic, Seokki Lee and Adriane Chapman
    Proceedings of the VLDB Endowment. 19, 2 (2025) , 252–264.
    details