References¶

Vortex is inspired by and–in some cases–directly based upon the existing, excellent work of many researchers and OSS developers. This page serves as a reference to the work that has influenced Vortex (and so we don’t keep asking one another to send links to the same papers over and over again).

Compression & Encodings¶

The FastLanes Compression Layout: Decoding > 100 Billion Integers per Second with Scalar Code — Afroozeh, Boncz. PVLDB 16(9), 2023.
Accelerating GPU Data Processing using FastLanes Compression — Afroozeh, Felius, Boncz. DaMoN 2024.
ALP: Adaptive Lossless floating-Point Compression — Afroozeh, Kuffo, Boncz. Proc. ACM Manag. Data 1(4), 2023.
FSST: fast random access string compression — Boncz, Neumann, Leis. PVLDB 13(12), 2020.

Scanning & Compute¶

Selection Pushdown in Column Stores using Bit Manipulation Instructions — Li, Lu, Chandramouli. Proc. ACM Manag. Data 1(2), 2023.
JSON Tiles: Fast Analytics on Semi-Structured Data — Durner, Leis, Neumann. SIGMOD 2021.
Memento Filter: A Fast, Dynamic, and Robust Range Filter — Eslami, Dayan. Proc. ACM Manag. Data 2(6), 2024.

Columnar File Formats¶

BtrBlocks: Efficient Columnar Compression for Data Lakes — Kuschewski, Sauerwein, Alhomssi, Leis. Proc. ACM Manag. Data 1(2), 2023.
Procella: Unifying serving and analytical data at YouTube — Chattopadhyay et al. PVLDB 12(12), 2019.

I/O & Cloud Storage¶

Exploiting Cloud Object Storage for High-Performance Analytics — Durner, Leis, Neumann. PVLDB 16(11), 2023.