Vessel trajectory data from the Automatic Identification System (AIS) is used widely in maritime analytics. Yet, analysis is difficult for non-expert users due to the incompleteness and complexity of AIS data. We present CLEAR, a knowledge-centric vessel trajectory analysis platform that aims to overcome these barriers. By leveraging the reasoning and generative capabilities of Large Language Models (LLMs), CLEAR transforms raw AIS data into complete, interpretable, and easily explorable vessel trajectories through a Structured Data-derived Knowledge Graph (SD-KG). As part of the demo, participants can configure parameters to automatically download and process AIS data, observe how trajectories are completed and annotated, inspect both raw and imputed segments together with their SD-KG evidence, and interactively explore the SD-KG through a dedicated graph viewer, gaining an intuitive and transparent understanding of vessel movements.

翻译：来自自动识别系统（AIS）的船舶轨迹数据在海事分析中应用广泛。然而，由于AIS数据的不完整性和复杂性，非专业用户难以进行分析。我们提出了CLEAR，一个以知识为中心的船舶轨迹分析平台，旨在克服这些障碍。通过利用大型语言模型（LLMs）的推理与生成能力，CLEAR通过一个结构化数据衍生的知识图谱（SD-KG），将原始AIS数据转化为完整、可解释且易于探索的船舶轨迹。作为演示的一部分，参与者可以配置参数以自动下载和处理AIS数据，观察轨迹如何被补全和标注，同时检查原始数据段与插补数据段及其对应的SD-KG证据，并通过专用的图谱查看器交互式探索SD-KG，从而获得对船舶运动的直观且透明的理解。