| 摘要: |
| 随着大数据时代的到来,数据分析的作用日益显著.它能够从海量数据中发现有价值的信息,从而更有效地指导用户决策.然而,数据分析流程中存在三大挑战:分析流程高耦合、交互接口种类多和探索分析高耗时.为了应对上述挑战,提出了基于自然语言交互的数据分析系统Navi.该系统采用模块化的设计原则,抽象出主流数据分析流程的3个核心功能模块:数据查询、可视化生成和可视化探索模块,从而降低系统设计的耦合度.同时,Navi以自然语言作为统一的交互接口,并通过一个任务调度器实现了各功能模块的有效协同.此外,为了解决可视化探索中搜索空间指数级和用户意图不明确的问题,提出了一种基于蒙特卡洛树搜索的可视化自动探索方法,并设计了基于可视化领域知识的剪枝算法和复合奖励函数,提高了搜索效率和结果质量.最后,通过量化实验和用户实验验证了Navi的有效性. |
| 关键词: 数据分析 数据查询 可视化 自然语言 蒙特卡洛树搜索 |
| DOI：10.13328/j.cnki.jos.007074 |
| 分类号: |
| 基金项目:国家自然科学基金(61925205,62232009,62102215);中国铁路总公司科技研究开发计划(K2022S005) |
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| Navi: Data Analysis System Powered by Natural Language Interaction |
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XIE Yu-Peng1, LUO Yu-Yu2, FENG Jian-Hua3
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1.Department of Computer Technology and Applications, Qinghai University, Xining 810016, China;2.The Hong Kong University of Science and Technology(Guangzhou), Guangzhou 511400, China;3.Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
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| Abstract: |
| With the advent of the big data era, the significance of data analysis has increasingly come to the forefront, showcasing its ability to uncover valuable insights from vast datasets, thereby enhancing the decision-making process for users. Nonetheless, the data analysis workflow faces three dominant challenges: high coupling in the analysis workflow, a plethora of interactive interfaces, and a time-intensive exploratory analysis process. To address these challenges, this study introduces Navi, a data analysis system powered by natural language interaction. Navi embraces a modular design philosophy that abstracts three core functional modules from mainstream data analysis workflows: data querying, visualization generation, and visualization exploration. This approach effectively reduces the coupling of the system. Meanwhile, Navi leverages natural language as a unified interactive interface to seamlessly integrate various functional modules through a task scheduler, ensuring their effective collaboration. Moreover, in order to address the challenges of exponential search space and ambiguous user intent in visualization exploration, this study proposes an automated approach for visualization exploration based on Monte Carlo tree search. In addition, a pruning algorithm and a composite reward function, both incorporating visualization domain knowledge, are devised to enhance the search efficiency and result quality. Finally, this study validates the effectiveness of Navi through both quantitative experiments and user studies. |
| Key words: data analysis data query visualization natural language Monte Carlo tree search |
