Carsten Binnig

Venues

Frequent coauthors

Papers

1. Demonstrating PIPE-X: Supporting Iterative Pipeline Development Through Explanations
   EDBT 2026 ·

   Nadja Geisler

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2. High-Performance DBMSs with io_uring: When and How to Use It
   VLDB 2026 ·

   Matthias Jasny

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3. IMPACT: Integrated Multimodal Pipeline for Rapid Accident Causality Tracking (Student Abstract)
   AAAI 2026 ·

   Vashu Chauhan

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4. PACMMOD V4, N1 (SIGMOD), February 2026 Editorial
   SIGMOD 2026 · Carsten Binnig
5. PACMMOD V4, N3 (SIGMOD), June 2026 Editorial
   SIGMOD 2026 · Carsten Binnig
6. PystachIO: Efficient Distributed GPU Query Processing with PyTorch over Fast Networks \u0026 Fast Storage
   VLDB 2026 ·

   Jigao Luo

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7. Redbench: Workload Synthesis From Cloud Traces
   VLDB 2026 ·

   Johannes Wehrstein

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8. Towards Executing Sloppy SQL Queries Over Tabular Data Lakes
   ICDE 2026 ·

   Jan-Micha Bodensohn

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9. Towards Multimodal Stream Processing Systems
   EDBT 2026 ·

   Uélison Jean Lopes dos Santos

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10. 21st International Workshop on Data Management on New Hardware (DaMoN)
    SIGMOD 2025 · Carsten Binnig
11. Benchmarking Analytical Query Processing in Intel SGXv2
    EDBT 2025 ·

    Adrian Lutsch

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12. Demonstrating PDSP-Bench: A Benchmarking System for Parallel and Distributed Stream Processing
    SIGMOD 2025 ·

    Pratyush Agnihotri

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13. Demonstration of Reflex: How SMPC Query Execution can be sped up through Efficient and Flexible Intermediate Result Size Trimming
    VLDB 2025 ·

    Long Gu

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14. GRACEFUL: A Learned Cost Estimator for UDFs
    ICDE 2025 ·

    Johannes Wehrstein

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15. How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks
    SIGMOD 2025 ·

    Roman Heinrich

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16. JOB-Complex: A Challenging Benchmark for Traditional & Learned Query Optimization
    VLDB 2025 ·

    Johannes Wehrstein

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17. JUSTINE (JUST-INsert Engine): Demonstrating Self-organizing Data Schemas
    VLDB 2025 ·

    Benjamin Hättasch

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18. Learning What Matters: Automated Feature Selection for Learned Cost Model in Parallel Stream Processing
    VLDB 2025 ·

    Pratyush Agnihotri

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19. Opening The Black-Box: Explaining Learned Cost Models For Databases
    VLDB 2025 ·

    Roman Heinrich

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20. PACMMOD V3, N4 (SIGMOD), September 2025: Editorial
    SIGMOD 2025 · Carsten Binnig
21. PACMMOD V3, N6 (SIGMOD), December 2025: Editorial
    SIGMOD 2025 · Carsten Binnig
22. Panel on AI for Future Databases: A New Beginning or a Boulevard of Broken Dreams?
    SIGMOD 2025 · Carsten Binnig
23. Panel on Neural Relational Data: Tabular Foundation Models, LLMs... or both?
    VLDB 2025 ·

    Paolo Papotti

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24. Unveiling Challenges for LLMs in Enterprise Data Engineering
    VLDB 2025 ·

    Jan-Micha Bodensohn

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25. Box2Go: Collaborative Interactive Infobox Filling
    WWW 2024 ·

    Benjamin Hättasch

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26. Costream: Learned Cost Models for Operator Placement in Edge-Cloud Environments
    ICDE 2024 ·

    Roman Heinrich

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27. Demonstrating CAESURA: Language Models as Multi-Modal Query Planners
    SIGMOD 2024 ·

    Matthias Urban

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28. ELEET: Efficient Learned Query Execution over Text and Tables
    VLDB 2024 ·

    Matthias Urban

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29. GOLAP: A GPU-in-Data-Path Architecture for High-Speed OLAP
    SIGMOD 2024 ·

    Nils Boeschen

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30. Generalizable Data Cleaning of Tabular Data in Latent Space
    VLDB 2024 ·

    Eduardo Souza dos Reis

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31. International Workshop on Data Management on New Hardware (DaMoN)
    SIGMOD 2024 · Carsten Binnig
32. LLMs for Data Engineering on Enterprise Data
    VLDB 2024 ·

    Jan-Micha Bodensohn

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33. Pythagoras: Semantic Type Detection of Numerical Data in Enterprise Data Lakes
    EDBT 2024 ·

    Sven Langenecker

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34. SmartNICs in the Cloud: The Why, What and How of In-network Processing for Data-Intensive Applications
    SIGMOD 2024 ·

    Faeze Faghih

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35. WikiDBs: A Large-Scale Corpus Of Relational Databases From Wikidata
    NeurIPS 2024 ·

    Liane Vogel

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36. ZERoTuNE: Learned Zero-Shot Cost Models for Parallelism Tuning in Stream Processing
    ICDE 2024 ·

    Pratyush Agnihotri

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37. Zero-sided RDMA: Network-driven Data Shuffling for Disaggregated Heterogeneous Cloud DBMSs
    SIGMOD 2024 ·

    Matthias Jasny

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