Vectorizing an In Situ Query Engine
Summary: Explores vectorizing an in situ, pre-load-free query engine with SIMD to reduce data-loading bottlenecks. Assesses vectorized scan parsing and a select-aggregate path, yielding notable speedups plus memory- and plan-related trade-offs. (summarized by gpt-5-nano on Feb 09 2026)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 959 | Rethinking SIMD Vectorization for In-Memory Databases | 2015 | SIGMOD | 0.00015034808 |
| 1,346 | NoDB: Efficient Query Execution on Raw Data Files | 2012 | SIGMOD | 0.00012472598 |
| 2,326 | Instant Loading for Main Memory Databases | 2013 | VLDB | 9.0271498e-05 |
| 2,764 | Parallel Data Analysis Directly on Scientific File Formats | 2014 | SIGMOD | 8.1607305e-05 |
| 2,975 | Parallel In-Situ Data Processing with Speculative Loading | 2014 | SIGMOD | 7.7871791e-05 |
| 3,548 | Adaptive Query Processing on RAW Data | 2014 | VLDB | 6.9798836e-05 |
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