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Non-Invasive Progressive Optimization for In-Memory Databases

Summary: Uses near-zero-cost CPU performance counters for non-invasive, runtime progressive optimization in in-memory databases. Fine-grained cost models trigger reoptimization and learn selectivity, sortedness, skew, and correlation, improving robustness substantially. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11493
Venue
VLDB
Year
2016
Pagerank
5.6322753e-05
Overall Rank
7,366 | 49.47%
DOI
10.14778/3007328.3007331

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zeuch_vldb16,
        title = {{Non-Invasive Progressive Optimization for In-Memory Databases}},
        author = {Zeuch, Steffen and Pirk, Holger and Freytag, Johann-Christoph},
        journal = {PVLDB},
        series = {{VLDB} '16},
        volume = {9},
        number = {14},
        pages = {1659--1670},
        doi = {10.14778/3007328.3007331},
        url = {https://doi.org/10.14778/3007328.3007331},
        year = {2016}
}

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