Incorporating Super-Operators in Big-Data Query Optimizers
Summary: Fuses shuffle-inducing Join, UnionAll, Spool, and GroupBy subqueries into parametric streaming super-operators, targeting dominant big-data costs. Abstract operator trees and matching avoid rule explosion; SCOPE reduces resource cost 1.7× and latency 1.5× without slower optimization. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jyoti Leeka (Microsoft)
- 2. Kaushik Rajan (Microsoft)
BibTeX Citation
@article{leeka_vldb20,
title = {{Incorporating Super-Operators in Big-Data Query Optimizers}},
author = {Leeka, Jyoti and Rajan, Kaushik},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {3},
pages = {348--360},
doi = {10.14778/3368289.3368299},
url = {https://doi.org/10.14778/3368289.3368299},
year = {2020}
}
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