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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)

Paper ID
5220
Venue
SIGMOD
Year
2016
Pagerank
4.1905499e-05
Overall Rank
11,858 | 17.59%
DOI
10.1145/2882903.2914829

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Showing 6 of 6 cited papers.

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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