Slalom: Coasting Through Raw Data via Adaptive Partitioning and Indexing
Summary: Slalom adaptively partitions raw files and builds lightweight, partition-specific indexes online by monitoring shifting query access patterns. Its in-situ engine cuts cumulative workload time and memory use, achieving 3–10× gains over prior systems and near-DBMS latency. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Matthaios Olma (EPFL)
- 2. Manos Karpathiotakis (EPFL)
- 3. Ioannis Alagiannis (Microsoft)
- 4. Manos Athanassoulis (Harvard University)
- 5. Anastasia Ailamaki (EPFL)
BibTeX Citation
@article{olma_vldb17,
title = {{Slalom: Coasting Through Raw Data via Adaptive Partitioning and Indexing}},
author = {Olma, Matthaios and Karpathiotakis, Manos and Alagiannis, Ioannis and Athanassoulis, Manos and Ailamaki, Anastasia},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {10},
pages = {1106--1117},
doi = {10.14778/3115404.3115410},
url = {https://doi.org/10.14778/3115404.3115410},
year = {2017}
}
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