DBScholar

Back to papers

Designing an Open Framework for Query Optimization and Compilation

Summary: Proposes an open, MLIR-based layered query compilation stack with interoperable IRs, enabling flexible, cross-layer optimization for data-centric code generation. LingoDB demonstrates reduced implementation effort, high performance, and low compilation latency by moving optimization into the compiler and supporting cross-domain optimization. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h9657cfd09a3c842b
Venue
VLDB
Year
2022
Pagerank
6.8697828e-05
Overall Rank
3,986 | 73.22%
DOI
10.14778/3551793.3551801
PDF
Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{jungmair_vldb22,
        title = {{Designing an Open Framework for Query Optimization and Compilation}},
        author = {Jungmair, Michael and Kohn, André and Giceva, Jana},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {11},
        pages = {2389--2401},
        doi = {10.14778/3551793.3551801},
        url = {https://doi.org/10.14778/3551793.3551801},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 20 of 20 citing papers.

Rank Citing Paper Year Venue Pagerank
5,991 Terabyte-Scale Analytics in the Blink of an Eye 2026 VLDB 5.9219616e-05
6,301 Declarative Sub-Operators for Universal Data Processing 2023 VLDB 5.8167709e-05
6,589 Maximus: A Modular Accelerated Query Engine for Data Analytics on Heterogeneous Systems 2025 SIGMOD 5.7401837e-05
6,747 Mitigating the Impedance Mismatch between Prediction Query Execution and Database Engine 2025 SIGMOD 5.6883491e-05
7,966 Excalibur: A Virtual Machine for Adaptive Fine-grained JIT-Compiled Query Execution based on VOILA 2023 VLDB 5.4141361e-05
8,990 nsDB: Architecting the Next Generation Database by Integrating Neural and Symbolic Systems 2024 VLDB 5.2418665e-05
9,483 Query Compilation Without Regrets 2024 SIGMOD 5.1686727e-05
9,596 The Key to Effective UDF Optimization: Before Inlining, First Perform Outlining 2025 VLDB 5.1543679e-05
9,663 BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach 2023 SIGMOD 5.142891e-05
9,800 Raqlet: Cross-Paradigm Compilation for Recursive Queries 2026 CIDR 5.1233734e-05
10,664 InferF: Declarative Factorization of AI/ML Inferences over Joins 2026 SIGMOD 4.9769913e-05
10,733 Scalable GPU Acceleration of Scalar Functions in Analytical Databases: Compilation, Benchmarking, and Optimization 2026 VLDB 4.9769913e-05
10,932 TQP++: Bridging ML Compilers and Analytical Query Processing on GPUs 2026 VLDB 4.9769913e-05
10,941 IMLane: Composable Framework for Efficient AI Function Execution in Database Engine 2026 VLDB 4.9769913e-05
11,046 Future-Proof Data Systems 2026 VLDB 4.9769913e-05
11,163 LingoDB-CT: Understanding LingoDB's Inner Workings 2025 SIGMOD 4.9769913e-05
11,351 Towards Designing Future-Proof Data Processing Systems 2025 VLDB 4.9769913e-05
11,474 Welding Natural Language Queries to Analytics IRs with LLMs 2024 CIDR 4.9769913e-05
11,771 Asymptotically Better Query Optimization Using Indexed Algebra 2023 VLDB 4.9769913e-05
11,785 Big Data Analytic Toolkit: A general-purpose, modular, and heterogeneous acceleration toolkit for data analytical engines 2023 VLDB 4.9769913e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers