About DeciDB
A research collaboration between the HUDA Lab at NYU Abu Dhabi and UMass Amherst, bringing optimization directly into SQL.
Research Foundation
DeciDB builds on a decade of research into in-database constrained optimization:
Scalable Package Queries in Relational Database Systems
Matteo Brucato, Azza Abouzied, Alexandra Meliou
VLDB 2016
Read PaperScalable Computation of High-Order Optimization Queries
Matteo Brucato, Azza Abouzied, Alexandra Meliou
Communications of the ACM, 2019
Read PaperScaling Package Queries to a Billion Tuples
Anh L. Mai, Pengyu Wang, Azza Abouzied, Matteo Brucato, Peter J. Haas, Alexandra Meliou
VLDB 2024
Read PaperThe Team
NYU Abu Dhabi
Principal Investigator, Director HUDA Lab
Anh Mai
PhD Student, Sketch-Refine algorithm
Filip Milisov
Research Assistant
Hatim Rehmanjee
Capstone Project 2025-2026
UMass Amherst
Co-PI, Package Queries
Peter J. Haas
Researcher, Scalable Package Queries
HUDA Lab
Human Data Interaction Lab
The HUDA Lab focuses on making data systems more accessible, interactive, and intelligent.
Research Areas:
- • Query interfaces for non-experts
- • Visual data exploration
- • Automated data analysis
- • Optimization in databases
Technical Details
Built With
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DuckDB — Embeddable analytical database engine
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Gurobi — Primary MILP/QP/QCQP solver, auto-detected at runtime (commercial; recommended for performance)
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HiGHS — Bundled open-source LP/MIP solver, used as fallback when Gurobi is unavailable
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SymbolicC++ — Computer algebra for constraint linearization
Current Status
master (working)
Future Development
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Sketch-Refine Algorithm
Scale beyond exact solving to very large datasets with bounded approximation error
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Python Bindings
pip install decidb— in-process Python package for embedding optimization queries in data pipelines -
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Broader Non-Linear Coverage
Extend automatic linearization beyond the current ABS, MIN/MAX,
<>,IN, and McCormick bilinear forms; full QCQP/SOCP on HiGHS