Most existing parametric query optimization (PQO) techniques rely on traditional query
optimizer cost models, which are often inaccurate and result in suboptimal query …

Existing machine learning (ML) approaches to automatically optimize database
management systems (DBMSs) only target a single configuration space at a time (eg, knobs …

This paper presents AdaChain, a learning-based blockchain framework that adaptively
chooses the best permissioned blockchain architecture in order to optimize effective …

Big data processing at the production scale presents a highly complex environment for
resource optimization (RO), a problem crucial for meeting performance goals and budgetary …

Many index advisors have recently been proposed to build indexes automatically to improve
query performance. However, they mainly consider performance improvement in static …

The performance of a query crucially depends on the ability of the query optimizer to choose
a good execution plan from a large space of alternatives. With the discovery of algebraic …

We propose cloud oracles, an alternative to machine learning for online optimization of
cloud configurations. Our cloud oracle approach guarantees complete accuracy and …

Traditional query optimizers are designed to be fast and stateless: each query is quickly
optimized using approximate statistics, sent off to the execution engine, and promptly …

The effectiveness of a cost-based query optimizer relies on the accuracy of selectivity
estimates. The execution plan generated by the optimizer can be extremely poor in reality …

Abstract Learning-based Parametric Query Optimization (PQO) methods excel in static
workloads with precise cached plan selection but struggle with dynamic workloads. When …