https://diprlab.github.io/author/primal-pappachan/Primal PappachanHugo Blox Builder (https://hugoblox.com)en-usFri, 06 Mar 2026 00:00:00 +0000https://diprlab.github.io/author/primal-pappachan/avatar_hu_1364ef7b42e60fc.pnghttps://diprlab.github.io/author/primal-pappachan/https://diprlab.github.io/dbrg/events/2026/winter/09/Fri, 06 Mar 2026 00:00:00 +0000https://diprlab.github.io/dbrg/events/2026/winter/09/<table> <tr> <td>Title</td> <td> BridgeScope: A Universal Toolkit for Bridging Large Language Models and Databases </td> </tr> <tr> <td>Authors</td> <td> Lianggui Weng, Dandan Liu, Rong Zhu, Bolin Ding, Jingren Zhou </td> </tr> <tr> <td>Abstract</td> <td> As large language models (LLMs) demonstrate increasingly powerful reasoning and orchestration capabilities, LLM-based agents are rapidly adopted 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 address these challenges, we introduce BridgeScope, a universal toolkit that bridges 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. This design enables more precise, LLM-friendly controls over database functionality. Second, it aligns tool implementations with database privileges and user-defined security policies to steer LLMs away from unsafe or unauthorized operations, which not only safeguards database security but also enhances task execution efficiency by enabling early identification and termination of infeasible tasks. Third, it introduces a proxy mechanism that supports seamless data transfer between tools, thereby bypassing the transmission bottlenecks via LLMs. 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 interact with databases more effectively. It reduces token usage by up to 80% through improved security awareness and uniquely supports data-intensive workflows beyond existing toolkits. These results establish BridgeScope as a robust foundation for next-generation intelligent data automation. </td> </tr> </table>https://diprlab.github.io/dbrg/events/2025/fall/06/Wed, 05 Nov 2025 00:00:00 +0000https://diprlab.github.io/dbrg/events/2025/fall/06/<table> <tr> <td> Title </td> <td> Delta Sharing: An Open Protocol for Cross-Platform Data Sharing </td> </tr> <tr> <td> Authors </td> <td> Krishna Puttaswamy, et al. </td> </tr> <tr> <td> Abstract </td> <td> Organizations across industries increasingly rely on sharing data to drive collaboration, innovation, and business performance. However, securely and efficiently sharing live data across diverse platforms and adhering to varying governance requirements remains a significant challenge. Traditional approaches, such as FTP and proprietary in-data-warehouse solutions, often fail to meet the demands of interoperability, cost, scalability, and low overhead. This paper introduces Delta Sharing, an open protocol we developed in collaboration with industry partners, to overcome these limitations. Delta Sharing leverages open formats like Delta Lake and Apache Parquet alongside simple HTTP APIs to enable seamless, secure, and live data sharing across heterogeneous systems. Since its launch in 2021, Delta Sharing has been adopted by over 4000 enterprises and supported by hundreds of major software and data vendors. We discuss the key challenges in developing Delta Sharing and how our design addresses them. We also present, to our knowledge, the first large-scale study of production data sharing workloads offering insights into this emerging data platform capability. </td> </tr> </table>https://diprlab.github.io/dbrg/events/2025/spring/01/Fri, 04 Apr 2025 00:00:00 +0000https://diprlab.github.io/dbrg/events/2025/spring/01/<table> <tr> <td> Title </td> <td> Navigating Labels and Vectors: A Unified Approach to Filtered Approximate Nearest Neighbor Search </td> </tr> <tr> <td> Authors </td> <td> Yuzheng Cai, Jiayang Shi, Yizhuo Chen, Weigue Zheng </td> </tr> <tr> <td> Abstract </td> <td> Given a query vector, approximate nearest neighbor search (ANNS) aims to retrieve similar vectors from a set of high-dimensional base vectors. However, many real-world applications jointly query both vector data and structured data, imposing label constraints such as attributes and keywords on the search, known as filtered ANNS. Effectively incorporating filtering conditions with vector similarity presents significant challenges, including index for dynamically filtered search space, agnostic query labels, computational overhead for label-irrelevant vectors, and potential inadequacy in returning results. To tackle these challenges, we introduce a novel approach called the Label Navigating Graph, which encodes the containment relationships of label sets for all vectors. Built upon graph-based ANNS methods, we develop a general framework termed Unified Navigating Graph (UNG) to bridge the gap between label set containment and vector proximity relations. UNG offers several advantages, including versatility in supporting any query label size and specificity, fidelity in exclusively searching filtered vectors, completeness in providing sufficient answers, and adaptability in integration with most graph-based ANNS algorithms. Extensive experiments on real datasets demonstrate that the proposed framework outperforms all baselines, achieving 10x speedups at the same accuracy. </td> </tr> </table>https://diprlab.github.io/publication/blue_crab_demo/Mon, 17 Mar 2025 00:00:00 +0000https://diprlab.github.io/publication/blue_crab_demo/https://diprlab.github.io/publication/accord/Mon, 13 May 2024 00:00:00 +0000https://diprlab.github.io/publication/accord/https://diprlab.github.io/publication/tattle-tale/Fri, 01 Jul 2022 00:00:00 +0000https://diprlab.github.io/publication/tattle-tale/