Ji Sun

Hongyi Wang, Ji Sun, Jinzhe Liang et al.

The ionic bonding across the lattice and ordered microscopic structures endow crystals with unique symmetry and determine their macroscopic properties. Unconventional crystals, in particular, exhibit non-traditional lattice structures or possess exotic physical properties, making them intriguing subjects for investigation. Therefore, to accurately predict the physical and chemical properties of crystals, it is crucial to consider long-range orders. While GNN excels at capturing the local environment of atoms in crystals, they often face challenges in effectively capturing longer-ranged interactions due to their limited depth. In this paper, we propose CrysToGraph ($\textbf{Crys}$tals with $\textbf{T}$ransformers $\textbf{o}$n $\textbf{Graph}$s), a novel transformer-based geometric graph network designed specifically for unconventional crystalline systems, and UnconvBench, a comprehensive benchmark to evaluate models' predictive performance on unconventional crystal materials such as defected crystals, low-dimension crystals and MOF. CrysToGraph effectively captures short-range interactions with transformer-based graph convolution blocks as well as long-range interactions with graph-wise transformer blocks. CrysToGraph proofs its effectiveness in modelling unconventional crystal materials in multiple tasks, and moreover, it outperforms most existing methods, achieving new state-of-the-art results on the benchmarks of both unconventional crystals and traditional crystals.

Ji Sun, Guoliang Li, Peiyao Zhou et al.

Existing unstructured data analytics systems rely on experts to write code and manage complex analysis workflows, making them both expensive and time-consuming. To address these challenges, we introduce AgenticData, an innovative agentic data analytics system that allows users to simply pose natural language (NL) questions while autonomously analyzing data sources across multiple domains, including both unstructured and structured data. First, AgenticData employs a feedback-driven planning technique that automatically converts an NL query into a semantic plan composed of relational and semantic operators. We propose a multi-agent collaboration strategy by utilizing a data profiling agent for discovering relevant data, a semantic cross-validation agent for iterative optimization based on feedback, and a smart memory agent for maintaining short-term context and long-term knowledge. Second, we propose a semantic optimization model to refine and execute semantic plans effectively. Our system, AgenticData, has been tested using three benchmarks. Experimental results showed that AgenticData achieved superior accuracy on both easy and difficult tasks, significantly outperforming state-of-the-art methods.

Wei Zhou, Ji Sun, Xuanhe Zhou et al.

In the financial industry, data is the lifeblood of operations, and DBAs shoulder significant responsibilities for SQL tuning, database deployment, diagnosis, and service repair. In recent years, both database vendors and customers have increasingly turned to autonomous database platforms in an effort to alleviate the heavy workload of DBAs. However, existing autonomous database platforms are limited in their capabilities, primarily addressing single-point issues such as NL2SQL, anomaly detection, and SQL tuning. Manual intervention remains a necessity for comprehensive database maintenance. GaussMaster aims to revolutionize this landscape by introducing an LLM-based database copilot system. This innovative solution is designed not only to assist developers in writing efficient SQL queries but also to provide comprehensive care for database services. When database instances exhibit abnormal behavior, GaussMaster is capable of orchestrating the entire maintenance process automatically. It achieves this by analyzing hundreds of metrics and logs, employing a Tree-of-thought approach to identify root causes, and invoking appropriate tools to resolve issues. We have successfully implemented GaussMaster in real-world scenarios, such as the banking industry, where it has achieved zero human intervention for over 34 database maintenance scenarios. In this paper, we present significant improvements in these tasks with code at https://gitcode.com/opengauss/openGauss-GaussMaster.

Yizhang Zhu, Liangwei Wang, Chenyu Yang et al.

The rapid advancement of large language models (LLMs) has spurred the emergence of data agents--autonomous systems designed to orchestrate Data + AI ecosystems for tackling complex data-related tasks. However, the term "data agent" currently suffers from terminological ambiguity and inconsistent adoption, conflating simple query responders with sophisticated autonomous architectures. This terminological ambiguity fosters mismatched user expectations, accountability challenges, and barriers to industry growth. Inspired by the SAE J3016 standard for driving automation, this survey introduces the first systematic hierarchical taxonomy for data agents, comprising six levels that delineate and trace progressive shifts in autonomy, from manual operations (L0) to a vision of generative, fully autonomous data agents (L5), thereby clarifying capability boundaries and responsibility allocation. Through this lens, we offer a structured review of existing research arranged by increasing autonomy, encompassing specialized data agents for data management, preparation, and analysis, alongside emerging efforts toward versatile, comprehensive systems with enhanced autonomy. We further analyze critical evolutionary leaps and technical gaps for advancing data agents, especially the ongoing L2-to-L3 transition, where data agents evolve from procedural execution to autonomous orchestration. Finally, we conclude with a forward-looking roadmap, envisioning the advent of proactive, generative data agents.