Research direction
This research programme has developed through four stages. Lemo replaces the traditional query optimiser with a learned optimiser for concurrent workloads, while RankPQO enhances the traditional query optimiser for parametric workloads by adding learning-to-rank. TATA transfers plan representations between database tasks, and ScalePQO brings shared, adaptive models into OceanBase.
Replacement
Learned latency estimation and reuse-aware plan generation for concurrent queries.
Enhancement
Adds learning-to-rank to the traditional query optimiser for parametric workloads.
Transfer
Query-plan representations transfer across database tasks.
Industrial deployment
Shared, adaptive models operate inside OceanBase.
Concurrent queries · ACM SIGMOD 2024
Concurrent queries can repeat the same computation while competing for shared resources. Lemo combines learned latency estimation with a shared buffer for intermediate subquery results, so cached results can be considered during plan generation. The system was implemented as a PostgreSQL prototype.
I am the first author and implemented the PostgreSQL prototype.
Parametric plans · VLDB 2025
Parameterized SQL templates may require different plans for different parameter bindings, while absolute latency is difficult to predict reliably. RankPQO combines plan enumeration with learning-to-rank for offline plan caching and online plan selection in PostgreSQL.
I am the first author and led the design and implementation of the query-optimisation work.
The paper reports up to 2.57× acceleration over the PostgreSQL optimiser and up to 1.36× over the strongest evaluated baseline.
Task transfer · VLDB 2026
Learned database components often require new labelled data for every task. TATA transfers query-plan representations across tasks by combining self-supervised plan reconstruction with database-aware generation of pseudo-labelled target data. It was evaluated when transferring from cost estimation to query optimisation and index selection.
I am a contact author of this work.
The paper reports up to a fivefold reduction in data-collection cost across the evaluated transfer settings.
Industrial system · VLDB 2026 Industry Track
Industrial parametric query optimisation must support many query templates and adapt when parameter distributions change. ScalePQO shares models across related templates and controls online model updates as workloads evolve. The system was implemented in OceanBase, an enterprise-grade distributed database originating from Ant Group.
I am the first author and led the technical development of the query-optimisation strand in the NTU–OceanBase collaboration.
Across six evaluated workloads, the paper reports up to 1.62× acceleration over OceanBase and up to 1.23× over RankPQO.