Slalom: Coasting Through Raw Data via Adaptive Partitioning and Indexing
Summary: Slalom enables in-situ processing with online partitioning and per-partition indexes guided by lightweight access-pattern monitoring. Its adaptive partitioning and indexing tailor layouts to workload shifts, yielding 3-10x faster queries than prior in-situ engines and DBMS-like latency on unpredictable access. (summarized by gpt-5-nano on Feb 09 2026)
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|---|---|---|---|---|
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| 12,519 | Scalable Continuous Query Processing by Tracking Hotspots | 2006 | VLDB | 4.1945683e-05 |
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| 11,850 | Vectorizing an In Situ Query Engine | 2016 | SIGMOD | 4.1945683e-05 |
| 1,343 | NoDB: Efficient Query Execution on Raw Data Files | 2012 | SIGMOD | 0.00012482538 |
| 2,973 | Parallel In-Situ Data Processing with Speculative Loading | 2014 | SIGMOD | 7.7902322e-05 |
| 3,548 | Adaptive Query Processing on RAW Data | 2014 | VLDB | 6.9859242e-05 |
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