Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The University of Melbourne（墨尔本大学）；Zhejiang University（浙江大学）；Ocean University of China（中国海洋大学）；Beijing Jiaotong University（北京交通大学）；Squirrel AI（松鼠AI）；Chongqing University（重庆大学）；University of Electronic Science and Technology of China（电子科技大学）；Hong Kong University of Science and Technology（香港科技大学）
Wei Chen, Yuanshao Zhu, Yanchuan Chang, Kang Luo, Haomin Wen, Lei Li, Yanwei Yu, Qingsong Wen, Chao Chen, Kai Zheng, Yunjun Gao, Yu Zheng, Xiaofang Zhou, Yuxuan Liang
Trajectory computing is a pivotal domain encompassing trajectory data management and mining, garnering widespread attention due to its crucial role in various practical applications such as location services, urban traffic, and public safety. Traditional methods, focusing on simplistic spatio-temporal features, face challenges of complex calculations, limited scalability, and inadequate adaptability to real-world complexities. In this paper, we present a comprehensive review of the development and recent advances in trajectory computing, from deep learning to the more recent large language models. We first define trajectory data and provide a brief overview of widely-used deep learning models. Systematically, we explore deep learning applications in trajectory management (pre-processing, storage, analysis, and visualization) and mining (trajectory-related forecasting, trajectory-related recommendation, trajectory classification, travel time estimation, anomaly detection, and mobility generation). Furthermore, we discuss emerging research directions and recent advancements in large models (represented by foundation models and large language models) for trajectory computing, which promise to reshape the next generation of trajectory computing. Additionally, we summarize application scenarios, public datasets, and toolkits. Finally, we outline current challenges in trajectory computing research and propose future directions. Relevant papers and open-source resources have been collated and are continuously updated at: https://github.com/yoshall/Awesome-Trajectory-Computing.
Re-Thinking Process Mining in the AI-Based Agents Era
RWTH Aachen University（亚琛工业大学）；Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）；Higher Institute of Management of Sousse, University of Sousse（苏塞大学苏塞高等管理学院）；Eindhoven University of Technology（埃因霍温理工大学）；Process Insights, ECE Group Services（Process Insights公司（ECE集团服务））；Microsoft（微软公司）
Advancing Chinese biomedical text mining with community challenges
Sichuan University（四川大学）；West China Hospital（华西医院）；Tongji University（同济大学）；Shanghai Business School（上海商学院）；Takeda Co. Ltd.（武田制药有限公司）；Harbin Institute of Technology（哈尔滨工业大学）
Text Classification using the Concept of Association Rule of Data Mining
Stamford University Bangladesh（孟加拉国斯坦福德大学）；International Islamic University Chittagong（吉大港国际伊斯兰大学）；Bangladesh University of Engineering & Technology（孟加拉工程技术大学）
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland University（萨尔大学）
Intelligent Cross-Organizational Process Mining: A Survey and New Perspectives
University of Oxford（牛津大学）；Peng Cheng Laboratory（鹏城实验室）；Institute for Infocomm Research, A*STAR（新加坡资讯通信研究院（新加坡科技研究局下属））；Fudan University（复旦大学）；Squirrel Ai Learning（松鼠AI智适应教育）
Mining Education Data to Predict Student's Retention: A comparative Study
Sri Venkateshwara University（斯里文卡特什瓦拉大学）；Dr. R. M. L. Awadh University（拉曼·拉胡尔·达斯·阿瓦德大学）；VBS Purvanchal University（维迪亚·巴胡·辛格·普尔万查尔大学）；J. P. Nagar（J.P.纳加尔）
Engineering Carbon Credits Towards A Responsible FinTech Era: The Practices, Implications, and Future
School of Electrical and Computer Engineering（电气与计算机工程学院）；The University of Sydney（悉尼大学）；Adelaide Business School（阿德莱德商学院）；The University of Adelaide（阿德莱德大学）；School of Computer Science and Engineering（计算机科学与工程学院）；University of New South Wales Sydney（新南威尔士大学悉尼分校）；The University of Sydney Business School（悉尼大学商学院）
Shedding Light on Blind Spots: Developing a Reference Architecture to Leverage Video Data for Process Mining
FIM Research Center, University of Applied Sciences Augsburg（奥格斯堡应用技术大学FIM研究中心）；FIM Research Center, University of Bayreuth（拜罗伊特大学FIM研究中心）；Project Group Business & Information Systems Engineering of the Fraunhofer FIT（弗劳恩霍夫应用信息处理研究所商业与信息系统工程项目组）
