weekly-papers-2026-11-14

Dynamic programming over subsets for a three-way join of EMP, DEPT and JOB: best single-relation access paths, then best two-relation plans per interesting order, skipping the DEPT–JOB Cartesian product, then the best three-relation plan.

Access Path Selection in a Relational Database Management System (Selinger et al., 1979)

Weekly Paper Notes — the Seminal Paper of the Week for the 2026-11-14 digest. Area: Databases. Authors: P. Griffiths Selinger, M. M. Astrahan, D. D. Chamberlin, R. A. Lorie, T. G. Price (IBM Research, San Jose) Published: Proceedings of the 1979 ACM SIGMOD International Conference on Management of Data, Boston, pp. 23–34. DOI: 10.1145/582095.582099 Why the paper still matters Codd’s relational model (our seminal pick in August) let users say what data they wanted and leave how to the system....

Raven architecture: a client engine talks gRPC to the catalog service (ERQL transpiler), which reads and writes catalog metadata and edge logs in the metadata store; a separate garbage collector with a graph store and in-memory graph engine consumes the edge logs.

Raven: Towards Unified File Management for Data Lakehouse Systems

Weekly Paper Notes — one of the top picks from the 2026-11-14 CS paper digest. Area: Databases. Authors: Keonwoo Oh (University of Maryland), Akshay Mehra (Dolby Labs), Joshua Kimball (Dolby Labs), Amol Deshpande (University of Maryland) arXiv: 2611.15975 · PDF · Code · Submitted to ICDE 2027 TL;DR Lakehouse metadata sits at one of two extremes. Open table formats (Iceberg, Delta Lake, Hudi) write everything as files next to the data....

Three panels. Pretrained versus scratch encoder: separate pretraining is optional. Encoder-free versus scratch encoder: dedicated vision capacity is key. Pre-fusion versus in-fusion capacity: vision capacity can move in-fusion.

Unbundling Multimodal Vision Encoding

Weekly Paper Notes — one of the top picks from the 2026-11-14 CS paper digest. Area: AI / ML. Authors: Ellis Brown, Chris Hoang (equal contribution), Shengyi Qian, Shang-Wen Li, Mike Lewis, Marjan Ghazvininejad, Luke Zettlemoyer, Lucas Beyer, Mengye Ren, Jakob Verbeek, Saining Xie, Hu Xu (Meta FAIR, New York University) arXiv: 2611.17334 · PDF · Project page TL;DR Most vision-language models bolt a separately pretrained image encoder (CLIP, SigLIP and relatives) onto a language model....