Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.
Mining Association Rules in Various Computing Environments: A Survey
Institute of Science, Banaras Hindu University（贝拿勒斯印度大学科学学院）；Faculty of Education, Banaras Hindu University（贝拿勒斯印度大学教育学院）；Mahila Maha Vidyalaya, Banaras Hindu University（贝拿勒斯印度大学Mahila Maha Vidyalaya）
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）
SparseST: Exploiting Data Sparsity in Spatiotemporal Modeling and Prediction
Department of Industrial and Systems Engineering, Rensselaer Polytechnic Institute（工业与系统工程系，罗切斯特理工学院）；Department of Electrical, Computer, and Systems Engineering and the Department of Computer Science, Rensselaer Polytechnic Institute（电气、计算机与系统工程系和计算机科学系，罗切斯特理工学院）；IBM Research（IBM研究院）
Survey on Strategic Mining in Blockchain: A Reinforcement Learning Approach
Zhongguancun Laboratory（中关村实验室）；Peking University（北京大学）；Beihang University（北京航空航天大学）；Hangzhou International Innovation Institute of Beihang University（北京航空航天大学杭州国际创新研究院）
Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Carnegie Mellon University（卡内基梅隆大学）；National University of Singapore（新加坡国立大学）；Griffith University（格里菲斯大学）；East China Normal University（华东师范大学）；University of New South Wales（新南威尔士大学）；Squirrel Ai Learning, USA（美国松鼠人工智能学习公司）；Nanyang Technology University（南洋理工大学）
Deep Learning for Spatio-Temporal Data Mining: A Survey
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Tsinghua University（清华大学）
A Survey and Taxonomy of Distributed Data Mining Research Studies: A Systematic Literature Review
Dian Nuswantoro University（迪安努斯万托罗大学）
内容：数据采掘（Data Mining，DM）方法已经在逐年发展，并且到今天为止，还存在DM技术的增强，其运行速度可以比传统方法快几倍，称为分布式数据采掘（Distributed Data Mining，DDM）。它实际上并不是数据处理中的一个新领域，但近年来这方面的研究受到了越来越多的关注。问题：关于DDM在高声誉期刊和会议上的出版物数量显着增加。这使得研究者很难对DDM有一个全面的了解，需要进一步的研究。解决方案：我们进行了系统的文献回顾，以映射以前在DDM领域的研究。我们的目标是通过识别DDM领域的差距以及热点领域本身来为新的研究提供动力。结果：通过对文献综述中提出的7个研究问题的回答，得出了一些结论。此外，本文还对DDM的研究领域进行了分类。最后，本文对2000年至2015年DDM的发展进行了系统的文献综述，以帮助未来的研究者对DDM的发展现状有一个全面的了解。
A Survey of Generative Techniques for Spatial-Temporal Data Mining
University of Hong Kong（香港大学）；Peking University（北京大学）；Nanyang Technological University（南洋理工大学）；Tsinghua University（清华大学）；Westlake University（西湖大学）；University of Queensland（昆士兰大学）；Huawei Cloud & AI（华为云与人工智能）；Aalborg University（奥尔堡大学）
Spatiotemporal data analysis with chronological networks
National Institute for Space Research（巴西国家空间研究院）；Humboldt University（洪堡大学）；University of Campinas（坎皮纳斯大学）；Indiana University（印第安纳大学）；Federal University of São Paulo（圣保罗联邦大学）；University of São Paulo（圣保罗大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）
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）
Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
University of Maryland Baltimore County（马里兰大学巴尔的摩分校）；NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions (iHARP)（NSF HDR极地地区数据与模型变革研究所）；University of North Texas（北得克萨斯大学）
