arXiv subjects

Lianggui Weng

Publications and source records attributed to Lianggui Weng.

5 recordsLinked to original sources

arXiv2026

QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics

Enterprise data analysis is emerging as a distinct frontier for autonomous agents. Compared with general-purpose interaction and software engineering, it operates in an open, ambiguous, and continuously evolving environment. These characteristics call for a data-agent architecture that treats semantics, methodology, execution, and evolution as first-class system concerns. To this end, we introduce QwenPaw-Data, an agentic data system designed for enterprise intelligent data analysis. QwenPaw-Data consolidates heterogeneous assets from warehouses, dashboards, documents, interaction logs, and historical tasks into reusable, governable, and evolvable analysis assets, then turns natural-language requests into end-to-end analytical workflows spanning data understanding, retrieval, analysis, report generation, and decision support. Its architecture decomposes the problem into three collaborative subsystems: DataBridge provides trustworthy semantic grounding through interconnected metadata, knowledge, and trace graphs; Skill-Hub codifies expert analytical methodology into reusable and verifiable skills; and Host materializes these evidence and method assets into controllable, artifact-centric runtime execution. Across these subsystems, semantics, methods, traces, and feedback are continuously deposited back into the system, forming a self-evolving asset flywheel. Experiments on public benchmarks and real-world industrial BI workloads show that QwenPaw-Data improves both verifiable data access capability and higher-level analytical quality, offering a practical foundation for reliable, traceable, and continuously improving enterprise data agents.

cs.AI↗

arXiv2026

Learned Query Optimizer in Alibaba MaxCompute: Challenges, Analysis, and Solutions

Existing learned query optimizers remain ill-suited to modern distributed, multi-tenant data warehouses due to idealized modeling assumptions and design choices. Using Alibaba's MaxCompute as a representative, we surface four fundamental, system-agnostic challenges for any deployable learned query optimizer: 1) highly dynamic execution environments that induce large variance in plan costs; 2) potential absence of input statistics needed for cost estimation; 3) infeasibility of conventional model refinement; and 4) uncertain benefits across different workloads. These challenges expose a deep mismatch between theoretical advances and production realities and demand a principled, deployment-first redesign of learned optimizers. To bridge this gap, we present LOAM, a one-stop learned query optimization framework for MaxCompute. Its design principles and techniques generalize and are readily adaptable to similar systems. Architecturally, LOAM introduces a statistics-free plan encoding that leverages operator semantics and historical executions to infer details about data distributions and explicitly encodes the execution environments of training queries to learn their impacts on plan costs. For online queries with unknown environments at prediction time, LOAM provides a theoretical bound on the achievable performance and a practical strategy to smooth the environmental impacts on cost estimations. For system operating, LOAM integrates domain adaptation techniques into training to generalize effectively to online query plans without requiring conventional refinement. Additionally, LOAM includes a lightweight project selector to prioritize high-benefit deployment projects. LOAM has seen up to 30% CPU cost savings over MaxCompute's native query optimizer on production workloads, which could translate to substantial real-world resource savings.

cs.DB↗

arXiv2025

BridgeScope: A Universal Toolkit for Bridging Large Language Models and Databases

As large language models (LLMs) demonstrate increasingly powerful reasoning and orchestration capabilities, LLM-based agents are rapidly proliferating for complex data-related tasks. Despite this progress, the current design of how LLMs interact with databases exhibits critical limitations in usability, security, privilege management, and data transmission efficiency. To resolve these challenges, we introduce BridgeScope, a universal toolkit bridging LLMs and databases through three key innovations. First, it modularizes SQL operations into fine-grained tools for context retrieval, CRUD execution, and ACID-compliant transaction management, enabling more precise and LLM-friendly functionality controls. Second, it aligns tool implementations with both database privileges and user security policies to steer LLMs away from unsafe or unauthorized operations, improving task execution efficiency while safeguarding database security. Third, it introduces a proxy mechanism for seamless inter-tool data transfer, bypassing LLM transmission bottlenecks. All of these designs are database-agnostic and can be transparently integrated with existing agent architectures. We also release an open-source implementation of BridgeScope for PostgreSQL. Evaluations on two novel benchmarks demonstrate that BridgeScope enables LLM agents to operate databases more effectively, reduces token usage by up to 80% through improved security awareness, and uniquely supports data-intensive workflows beyond existing toolkits, establishing BridgeScope as a robust foundation for next-generation intelligent data automation.

cs.DB↗

arXiv2021

PM-LSH: a fast and accurate in-memory framework for high-dimensional approximate NN and closest pair search

Nearest neighbor (NN) search is inherently computationally expensive in high-dimensional spaces due to the curse of dimensionality. As a well-known solution, locality-sensitive hashing (LSH) is able to answer c-approximate NN (c-ANN) queries in sublinear time with constant probability. Existing LSH methods focus mainly on building hash bucket-based indexing such that the candidate points can be retrieved quickly. However, existing coarse-grained structures fail to offer accurate distance estimation for candidate points, which translates into additional computational overhead when having to examine unnecessary points. This in turn reduces the performance of query processing. In contrast, we propose a fast and accurate in-memory LSH framework, called PM-LSH, that aims to compute c-ANN queries on large-scale, high-dimensional datasets. First, we adopt a simple yet effective PM-tree to index the data points. Second, we develop a tunable confidence interval to achieve accurate distance estimation and guarantee high result quality. Third, we propose an efficient algorithm on top of the PM-tree to improve the performance of computing c-ANN queries. In addition, we extend PM-LSH to support closest pair (CP) search in high-dimensional spaces. We again adopt the PM-tree to organize the points in a lowdimensional space, and we propose a branch and bound algorithm together with a radius pruning technique to improve the performance of computing c-approximate closest pair (c-ACP) queries. Extensive experiments with real-world data offer evidence that PM-LSH is capable of outperforming existing proposals with respect to both efficiency and accuracy for both NN and CP search.

cs.DB↗

arXiv2021

REPOSE: Distributed Top-k Trajectory Similarity Search with Local Reference Point Tries

Trajectory similarity computation is a fundamental component in a variety of real-world applications, such as ridesharing, road planning, and transportation optimization. Recent advances in mobile devices have enabled an unprecedented increase in the amount of available trajectory data such that efficient query processing can no longer be supported by a single machine. As a result, means of performing distributed in-memory trajectory similarity search are called for. However, existing distributed proposals suffer from either computing resource waste or are unable to support the range of similarity measures that are being used. We propose a distributed in-memory management framework called REPOSE for processing top-k trajectory similarity queries on Spark. We develop a reference point trie (RP-Trie) index to organize trajectory data for local search. In addition, we design a novel heterogeneous global partitioning strategy to eliminate load imbalance in distributed settings. We report on extensive experiments with real-world data that offer insight into the performance of the solution, and show that the solution is capable of outperforming the state-of-the-art proposals.

cs.DB↗