Peng Cai

The SQL-based exploratory data analysis has garnered significant attention within the data analysis community. The emergence of large language models (LLMs) has facilitated the paradigm shift from manual to automated data exploration. However, existing methods generally lack the ability for cross-domain analysis, and the exploration of LLMs capabilities remains insufficient. This paper presents TiInsight, a SQL-based automated cross-domain exploratory data analysis system. First, TiInsight offers a user-friendly GUI enabling users to explore data using natural language queries. Second, TiInsight offers a robust cross-domain exploratory data analysis pipeline: hierarchical data context (i.e., HDC) generation, question clarification and decomposition, text-to-SQL (i.e., TiSQL), and data visualization (i.e., TiChart). Third, we have implemented and deployed TiInsight in the production environment of PingCAP and demonstrated its capabilities using representative datasets.

Existing database knob tuning methods evaluate the actual performance of each configuration by fully executing the entire workload. However, our experimental analysis reveals that this exhaustive execution approach significantly limits tuning efficiency, particularly when dealing with underperforming configurations. To address this issue, we propose ESTune, which is designed to early-stop the execution of poorly performing configurations. ESTune approximates the actual performance of these configurations using high-confidence predicted values generated from partially executed workload data and configuration knob settings. This strategy significantly reduces the evaluation time for underperforming configurations while maintaining the overall tuning effectiveness. The high-confidence predicted values are produced by a Hybrid Bayesian Neural Network (HBNN), which models the performance distribution with respect to different knob configurations. To address the challenge of limited training data commonly encountered in database knob tuning, ESTune integrates a Model-Agnostic Meta-Learning (MAML), thereby enhancing the few-shot learning capability of the HBNN. Extensive evaluations on a wide range of workloads consistently demonstrate that ESTune improves the tuning efficiency of existing methods.

Isolation Levels (IL) act as correct contracts between applications and database management systems (DBMSs). The complex code logic and concurrent interactions among transactions make it hard to expose violations of various ILs stated by DBMSs. With the recent proliferation of new DBMSs, especially the cloud ones, there is an urgent demand for a general way to detect bugs violating various ILs. The core challenges come from the requirements of: (a) lightweight (verifying without modifying the application logic in workloads and the source code of DBMSs), (b) generality (verifying various ILs), and (c) efficiency (performing efficient verification on a long running workload). To this end, we propose a powerful and practical bug-finding tool Leopard. For lightweight, we propose to infer transaction dependencies based on the time intervals of operations collected from the client-side, without modifying the source code of DBMSs. For generality, based on a thorough analysis of existing concurrency control protocols, we summarize and abstract four mechanisms which can implement ILs in all commercial DBMSs we have investigated. For efficiency, we design a two-level pipeline to organize and sort massive time intervals in a time and memory conservative way; we propose a mechanism-mirrored verification to simulate the concurrency control protocols implemented in DBMSs for high throughputs; From experimental results, Leopard outperforms existing methods Cobra and Elle. In practice, Leopard has a superpower to verify various ILs on any workload running on all commercial DBMSs. Moreover, it has discovered 49 bugs undetected by other existing methods.

Previous learning-based parameter query optimization (PQO) methods excel in static workloads by precisely selecting optimal plans in a cache with a fixed set of representative plans. However, these methods struggle in dynamic workloads because they cannot predict over dynamically changed plan caches. These queries that fall outside the previously observed query parameter distribution have the risk of reusing suboptimal plans. Unlike traditional PQO methods that learn mappings from parametric query parameters to a fixed set of execution plans, our adaptive PQO framework ( APQO ) takes both query parameters and the plans themselves as model inputs, thereby handling variable numbers of plans in dynamic workloads. By embedding plan representations through representation learning, we pre-train a foundation model offline, enabling APQO to acquire a generalizable plan performance prediction model. Leveraging the foundation model's predictive capability along with a hybrid data augmentation strategy, we train an online calibration model with minimal training data for distribution-shifted new queries, rapidly adapting knowledge for reusing new plans. APQO is natively designed to handle the characteristics of dynamic workloads. Experimental results show that APQO outperforms existing PQO methods in dynamic workloads, achieving a higher cache hit ratio and significantly reducing query latency.

Electric bikes powered by lithium-ion batteries are increasingly used in smart cities to promote sustainable mobility and efficient delivery services. However, limited battery range and slow plug-in charging remain key challenges. Shared electric bike battery systems, facilitated by battery swapping stations, offer a promising solution by enabling quick and efficient battery replacements. However, their success hinges on accurate anomaly detection, battery health estimation and remain range prediction. These tasks remain challenging due to data scarcity, battery diversity and environmental variability. Here we show that a large-scale lithium-ion battery model trained on over ten million battery time series data enables robust and adaptable battery management across diverse real-world scenarios. The model learns complex battery behavior through unsupervised pretraining. Importantly, after efficient finetuning, the model significantly outperforms existing approaches in the three critical tasks. Deployed on cloud servers, our model enables real-time data processing, enhancing the safety, reliability and efficiency of battery swapping services. This advancement accelerates electric bike adoption, fostering sustainable urban mobility and green smart city development.

Index tuning is crucial for optimizing database performance, and selecting the optimal index configuration relies on accurately estimating the benefits of each candidate index. However, existing learning-based works face key limitations-including limited accuracy, overfitting risks, and lack of estimation confidence-hindering practical deployment. Especially when a new workload follows a different distribution (a.k.a. ''out-of-distribution'', OOD), previously trained models become inaccurate and blind trust on their estimations can lead to severe performance regression. Therefore, this paper proposes Rainbow, a novel framework that more accurately estimates index benefits under in-distribution scenarios while also providing uncertainty quantification to adapt to OOD workloads. We propose a graph-based encoder with Graph Transformer for expressive and efficient feature encoding, directly leveraging the original query plan's structural information to capture the global and local impact of indexes. Unlike existing deterministic estimators designed to entirely replace the what-if caller, we employ a Bayesian neural network (BNN) whose probabilistic nature enhances robustness and whose weight distributions provide principled uncertainty quantification. This allows our BNN estimator to fall back to the what-if caller when necessary, avoiding performance regression. Extensive experiments demonstrate that Rainbow not only outperforms state-of-the-art estimators in accuracy and index tuning quality when integrated with index advisors, but also shows robustness to OOD workloads.