This survey paper presents a comprehensive analysis of crime prediction methodologies, exploring the various techniques and technologies utilized in this area. The paper covers the statistical methods, machine learning algorithms, and deep learning techniques employed to analyze crime data, while also examining their effectiveness and limitations. We propose a methodological taxonomy that classifies crime prediction algorithms into specific techniques. This taxonomy is structured into four tiers, including methodology category, methodology sub-category, methodology techniques, and methodology sub-techniques. Empirical and experimental evaluations are provided to rank the different techniques. The empirical evaluation assesses the crime prediction techniques based on four criteria, while the experimental evaluation ranks the algorithms that employ the same sub-technique, the different sub-techniques that employ the same technique, the different techniques that employ the same methodology sub-category, the different methodology sub-categories within the same category, and the different methodology categories. The combination of methodological taxonomy, empirical evaluations, and experimental comparisons allows for a nuanced and comprehensive understanding of crime prediction algorithms, aiding researchers in making informed decisions. Finally, the paper provides a glimpse into the future of crime prediction techniques, highlighting potential advancements and opportunities for further research in this field
CrimeAlarm: Towards Intensive Intent Dynamics in Fine-grained Crime Prediction
Wuhan University of Technology（武汉理工大学）；University of Technology Sydney（悉尼科技大学）；University of Southern Queensland（南昆士兰大学）；Education University of Hong Kong（香港教育大学）
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）
OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset
Howard Community College（霍华德社区学院）；UC Berkeley（加州大学伯克利分校）；University of New South Wales（新南威尔士大学）；Stanford University（斯坦福大学）；Oracle Corporation（甲骨文公司）；University of Texas（德克萨斯大学）；New York University（纽约大学）
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。
ChemMiner: A Large Language Model Agent System for Chemical Literature Data Mining
The Chinese University of Hong Kong, New Territories, Hong Kong SAR（香港中文大学）；Zhejiang Lab, Zhejiang University, Hangzhou, China（浙江大学杭州实验室）；Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, China（中国科学院杭州医学研究所）
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）
Attack Pattern Mining to Discover Hidden Threats to Industrial Control Systems
Singapore University of Technology and Design, Singapore（新加坡科技设计大学）；Newcastle University, Newcastle, United Kingdom（纽卡斯尔大学）；Bahria University, Pakistan（巴里亚大学）
MSCMNet: Multi-scale Semantic Correlation Mining for Visible-Infrared Person Re-Identification
School of Computer, Jiangsu University of Science and Technology（江苏科技大学计算机学院）；School of Computer Science and Communication Engineering, Jiangsu University（江苏大学计算机科学与通信工程学院）；School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；School of Digital Media, Jiangnan University（江南大学数字媒体学院）
