Xu Chen

Learned Query Optimization (LQO) has emerged as a promising paradigm for improving database performance, with reported speedups of 2× to over 10× on standard benchmarks. Existing evaluations, however, remain largely latency-centric and leave the energy debt from data collection, model training, and online inference less visible. This paper presents E-LQO, the first comprehensive framework for evaluating energy consumption across the complete LQO lifecycle: data collection or preparation, model training, model inference, and plan execution. We formalize two domain-specific metrics— Energy Return on Investment (EROI) and Energy Payback Time (EPT)—to quantify both per-cycle efficiency and the amortization horizon of learned optimizers. Across seven LQO methods on fixed-scale JOB and on TPC-H/TPC-DS up to 100 GB, we find that lifecycle energy is governed less by whether an optimizer is learned than by how it obtains supervision. Execution-based systems buy plan quality through active exploration and carry a large upfront energy debt; passive/log-driven systems can repay much faster with reusable logs or labels, but this advantage depends on incremental-cost accounting and often comes with weaker latency gains on light workloads. We further show that total online inference energy, including candidate-plan generation, enables symmetric accounting, and that roughly 50% template coverage is the energy-optimal exploration region for the execution-based methods we study. E-LQO provides practitioners with actionable guidance for energy-conscious deployment decisions and exposes optimization opportunities for sustainable learned query optimization.

Velocity control in autonomous driving is an emerging technology that has achieved rapid progress over the last decade. However, existing velocity control studies ignore cascading disturbances in multi-lane scenarios and usually ignore the negative impact caused by harsh velocity decisions. To address these issues, we propose a reinforcement learning-based framework, called RISE (contRol velocIty for autonomouS vEhicle) to make velocity decisions for an autonomous vehicle in multi-lane traffic scenarios. To detect latent disturbances in the traffic flow, we propose a novel state encoder to learn the spatio-temporal correlation between different vehicles based on a well-designed impact graph. Afterward, we introduce an actor-critic paradigm to make velocity decisions with the aid of a hybrid reward function considering four optimization objectives: safety, efficiency, comfort, and impact. In particular, the impact term can penalize the harsh decisions of the autonomous vehicle, thus encouraging it to reduce the negative impacts on traffic flow. Further, we propose an improved RISE (RISE++) framework that incorporates a motion prediction model to augment state features for reasonable decisions, a modification of the reward function for energy efficiency, and a multi-worker paradigm for training efficiency. Extensive experiments offer evidence that the proposed framework can advance the state of the art in terms of effectiveness and efficiency.

In the realm of big data and cloud analytics, efficiently managing and retrieving high-dimensional data presents a critical challenge. Traditional indexes often struggle with the storage overhead inherent in large datasets. There is a growing interest in the adoption of Small Materialize Aggregation (SMA) among cloud database vendors due to its ability to maintain lightweight block-level metadata, facilitating efficient block skipping. However, SMA performance relies heavily on data layout. This is especially critical in scenarios with wide tables containing hundreds of dimensions, where the curse of dimensionality exacerbates the issue. In this paper, we propose AdaCurve , a novel approach aimed at enhancing block skipping in high-dimensional datasets through adaptive optimization of data layout. Unlike conventional static and non-adaptive space-filling curves (SFCs), AdaCurve leverages machine learning to develop an adaptive curve---a dynamically adjusting optimal projection function tailored to high-dimensional workloads and data characteristics. We introduce an attention-based network to handle high-dimensional data and a learnable objective for training adaptive curves in an end-to-end manner. Extensive experiments conducted on the Spark with real-world datasets demonstrate the effectiveness of AdaCurve . We have shown that AdaCurve effectively scales to datasets with dimensions of up to 1,000 columns, achieving a 2.8× improvement in block skipping compared to SFCs.

The performance of modern database management systems highly relies on hundreds of adjustable knobs. Traditionally, these knobs are manually adjusted by database administrators, a process that is both inefficient and ineffective for tuning large-scale databases in cloud environments. Recent research has explored the use of machine learning techniques to enable the automatic tuning of database configurations. Although most existing learning-based methods achieve satisfactory results on static workloads, they often experience performance degradation and low sampling efficiency in real-world environments. According to our study, this is primarily due to a lack of safety guarantees during the configuration sampling process. To address the aforementioned issues, we propose SafeTune, an online tuning system that adapts to dynamic workloads. Our core idea is to filter out a large number of configurations with potential risks during the configuration sampling process. We employ a two-stage filtering approach: The first stage utilizes a semi-supervised outlier ensemble with feature learning to achieve high-quality feature representation. The second stage employs a ranking-based classifier to refine the filtering process. In addition, to alleviate the cold-start problem, we leverage the historical tuning experience to provide high-quality initial samples during the initialization phase. We conducted comprehensive evaluations on static and dynamic workloads. In comparison to offline baseline methods, SafeTune reduces 95.6%-98.6% unsafe configuration suggestions. In contrast with state-of-the-art methods, SafeTune has improved cumulative performance by 10.5%-46.6% and tuning speed by 15.1%-35.4%.

A series of studies apply machine learning to assist cost-based query optimizers in DBMS, emphasizing incorporating uncertainty predictions to guide decision-making. While these approaches have demonstrated advancement in some benchmarks, their drawbacks, such as unstable performance, stem from the inherent challenges of using machine learning models to predict the cost of execution plans and the lack of exploration of the intrinsic characteristics of suboptimal plans. In this paper, we introduce an alert system for query optimization, which is built upon cost models to reduce the selection of regressed plans. The key insight is that there are differences in the predictive uncertainty that lead to query optimization and the regression of execution plans. We investigate the causes of these differences in uncertainty and design a discriminator to filter out execution plans with higher risks of regression. The alert system can be integrated with various cost models, enhancing the robustness of query optimizers. In our experiments, the system further reduces execution time by 20% compared to learned optimizers. Meanwhile, the proportion of optimized queries reduced by the alert system is just 15% of the proportion of regressed queries diminished.

Automation in road vehicles is an emerging technology that has developed rapidly over the last decade. There have been many inter-disciplinary challenges posed on existing transportation infrastructure by autonomous vehicles. In this paper, we conduct an algorithmic study on when and how an autonomous vehicle should change its lane, which is a fundamental problem in vehicle automation field and root cause of most ‘phantom’ traffic jams. We propose a prediction-and-decision framework, called Cheetah ( Ch ang e lan e smar t for a utonomous ve h icle), which aims to optimize the lane changing maneuvers of autonomous vehicle while minimizing its impact on surrounding vehicles. In the prediction phase, Cheetah learns the spatio-temporal dynamics from historical trajectories of surrounding vehicles with a deep model (GAS-LED model) and predict their corresponding actions in the near future. A global attention mechanism and state sharing strategy are also incorporated to achieve higher accuracy and better convergence efficiency. Then in the decision phase, Cheetah looks for optimal lane change maneuvers for the autonomous vehicle by taking into account a few factors such as speed, impact on other vehicles and safety issues. A tree-based adaptive beam search algorithm is designed to reduce the search space and improve accuracy. In order to make our framework applicable to more scenarios, we further propose an improved Cheetah (Cheetah ⁺ ) framework that makes the autonomous vehicle adapt for exiting a road and meet the requirement for driving comfort. Extensive experiments offer evidence that the proposed framework can advance the state of the art in terms of effectiveness and efficiency.