Federated computing has emerged as a promising approach to address the data isolation problem, enabling multiple data owners to utilize secure multi-party computation (MPC) to collaboratively process queries while keeping the data decentralized, private, and secret. However, existing studies primarily focused on federated queries over structural data, which does not apply to non-structural road network queries prevalent in daily travel scenarios. To tackle this limitation, this paper proposes FedRoad, the first traffic data federation with secure and efficient road network shortest-path queries over it. In this context, the network topology is shared while each silo (e.g., mobility services platform) holds an individual traffic observation of edge weights (e.g., vehicle speeds), where we search the path with minimum joint weights (e.g., the least traveling time). To ensure security, we implement a secret-sharing-based MPC operator to secretly compare joint path weights and achieve a secure federated shortest-path search based on it. To improve the efficiency over road network structures, we (1) first minimize the search iterations by proposing federated shortcut indices and effective federated lower-bound estimation methods, (2) then reduce the cost in each iteration by designing a priority queue structure dedicated to minimizing the expensive MPC comparison operations. Extensive experiments demonstrate that FedRoad significantly outperforms the baselines$(100\times \text{faster})$and is practical for usage (sub-second level running time).

Semi-structured tables, widely used in real-world applications (e.g., financial reports, medical records, transactional orders), often involve flexible and complex layouts (e.g., hierarchical headers and merged cells). These tables generally rely on human analysts to interpret table layouts and answer relevant natural language questions, which is costly and inefficient. To automate the procedure, existing methods face significant challenges. First, methods like NL2SQL require converting semi-structured tables into structured ones, which often causes substantial information loss. Second, methods like NL2Code and multi-modal LLM QA struggle to understand the complex layouts of semi-structured tables and cannot accurately answer corresponding questions. To this end, we propose ST-Raptor, a tree-based framework for semi-structured table question answering ( semi-structured table QA ) using large language models. First, we introduce the Hierarchical Orthogonal Tree (HO-Tree), a structural model that captures complex semi-structured table layouts, along with an effective algorithm for constructing the tree by identifying headers, content values, and their implicit relationships. Second, we define a set of basic tree operations to guide LLMs in executing common QA tasks. Given a user question, ST-Raptor decomposes it into simpler sub-questions, generates corresponding tree operation pipelines, and conducts operation-table alignment for accurate pipeline execution. Third, we incorporate a two-stage verification mechanism: (1) forward validation checks the correctness of execution steps, while (2) backward validation evaluates answer reliability by reconstructing queries from predicted answers. To benchmark the performance, we present SSTQA, a dataset of 764 questions over 102 real-world semi-structured tables. Experiments show that ST-Raptor outperforms nine baselines by up to 20% in answer accuracy. The code is available at https://github.com/weAIDB/ST-Raptor.

Automatic dialect translation reduces the complexity of database migration, which is crucial for applications interacting with multiple database systems. However, rule-based translation tools (e.g., SQLGlot, jOOQ, SQLines) are labor-intensive to develop and often (1) fail to translate certain operations, (2) produce incorrect translations due to rule deficiencies, and (3) generate translations compatible with some database versions but not the others. In this paper, we investigate the problem of automating dialect translation with large language models (LLMs). There are three main challenges. First, queries often involve lengthy content (e.g., excessive column values) and multiple syntax elements that require translation, increasing the risk of LLM hallucination. Second, database dialects have diverse syntax trees and specifications, making it difficult for cross-dialect syntax matching. Third, dialect translation often involves complex many-to-one relationships between source and target operations, making it impractical to translate each operation in isolation. To address these challenges, we propose an automatic dialect translation system CrackSQL. First, we propose Functionality-based Query Processing that segments the query by functionality syntax trees and simplifies the query via (i) customized function normalization and (ii) translation-irrelevant query abstraction. Second, we design a Cross-Dialect Syntax Embedding Model to generate embeddings by the syntax trees and specifications (of certain version), enabling accurate query syntax matching. Third, we propose a Local-to-Global Dialect Translation strategy, which restricts LLM-based translation and validation on operations that cause local failures, iteratively extending these operations until translation succeeds. Experiments show CrackSQL significantly outperforms existing methods (e.g., by up to 77.42%). The code is available at https://github.com/weAIDB/CrackSQL.

Many index advisors have recently been proposed to build indexes automatically to improve query performance. However, they mainly consider performance improvement in static scenarios. Their robustness, i.e., stable performance in dynamic scenarios (e.g., with minor workload changes), has not been well investigated. This paper addresses the challenges of assessing the index advisor's robustness from the following aspects. First, we introduce perturbation-based workloads for robustness assessment and identify three typical perturbation constraints that occur in real scenarios. Second, with the perturbation constraints, we formulate the generation of perturbed queries as a sequence-to-sequence problem and propose TRAP (Tailored Robustness assessment via Adversarial Perturbation) to pinpoint the performance loopholes of index advisors. Third, to generalize to various index advisors, we place TRAP in an opaque-box setting (i.e., with little knowledge of the index advisors' internal design), and we propose a two-phase training paradigm to efficiently train TRAP without elaborately annotated data. Fourth, we conduct comprehensive robustness assessments on standard benchmarks and real workloads for ten existing index advisors. Our findings reveal that these index advisors are vulnerable to the workloads generated by TRAP. Finally, based on the assessment results, we shed light on insights to enhance the robustness of different index advisors. For example, learning-based index advisors can benefit from adopting a fine-grained state representation and a candidate pruning strategy.

Index advisors aim to improve workload performance by judiciously selecting an appropriate set of indexes. Various heuristic-based and learning-based methods have been proposed. However, there lacks a comprehensive assessment of existing index advisors, i.e., their advantages, limitations, and application scenarios. In this work, we conduct an in-depth study of existing index advisors in five key aspects. First, we initiate an end-to-end analysis, i.e., a completed analysis throughout the entire workflow of index advisors. We decompose index advisors into three essential building blocks, establish a taxonomy to classify methods used in each block, and analyze the strengths and weaknesses of these methods. Second, we develop a unified open-source testbed, implementing seventeen index advisors across eleven open-source or real-world datasets. We enable customizable configurations to meet diverse testing requirements. Third, we conduct an extensive assessment of index advisors across database systems in various scenarios. We evaluate their adaptability and robustness, identifying practical application scenarios. Fourth, we undertake a fine-grained ablation study by investigating variants of each building block. We identify effective variants and pinpoint significant factors impacting index advisors' performance via explainable machine-learning techniques. Lastly, we consolidate our findings that could shed light on research directions to advance the future development of index advisors.

Index advisors have become an essential tool to optimize index selection and accelerate query processing. Various index advisors have been developed in recent years, and comprehensively assessing their performance from multiple aspects is necessary. In this demonstration, we introduce VITA, a user-friendly and informative tool for interactively Visualizing, Testing, and Analyzing index advisors. For a user-given workload, VITA can visualize the main steps of the index selection procedure in ten existing index advisors to facilitate the management of index advisors. Moreover, VITA can assess the index advisor's robustness w.r.t. workload drift by generating testing workloads, i.e., potentially future workloads that may damage the index advisor's performance. Finally, VITA provides a comparative analysis across index advisors on four aspects, including the index advisor's utility (i.e., the ratio of the reduced workload cost), robustness (i.e., the performance under dynamic workload), overhead (i.e., the time to acquire the final configuration), and scalability (i.e., the volume of the enumerated index candidates). Therefore, VITA can thoroughly compare existing index advisors to help users determine the most suitable index advisor that meets their requirements. VITA is now being integrated into the openGauss platform as a plug-in.