Intelligent spatial-temporal data analysis,leveraging data such as multivariate time series and geographic information,provides researchers with powerful tools to uncover multiscale patterns and enhance decision-makin...Intelligent spatial-temporal data analysis,leveraging data such as multivariate time series and geographic information,provides researchers with powerful tools to uncover multiscale patterns and enhance decision-making processes.As artificial intelligence advances,intelligent spatial-temporal algorithms have found extensive applications across various disciplines,such as geosciences,biology,and public health.1 Compared to traditional methods,these algorithms are data driven,making them well suited for addressing the complexities of modeling real-world systems.However,their reliance on substantial domain-specific expertise limits their broader applicability.Recently,significant advancements have been made in spatial-temporal large models.Trained on large-scale data,these models exhibit a vast parameter scale,superior generalization capabilities,and multitasking advantages over previous methods.Their high versatility and scalability position them as promising super hubs for multidisciplinary research,integrating knowledge,intelligent algorithms,and research communities from different fields.Nevertheless,achieving this vision will require overcoming numerous critical challenges,offering an expansive and profound space for future exploration.展开更多
基金Ye YUAN is supported by the National Key R&D Program of China(Grant No.2022YFB2702100)the National Natural Science Foundation of China(NSFC)(Grant Nos.61932004,62225203,U21A20516)+5 种基金Hangxu JI is supported by the NSFC(Grant No.62402096)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515110268)Yishu WANG is supported by the NSFC(Grant No.62302084)Yuliang MA is supported by the NSFC(Grant No.62002054)the Fundamental Research Funds for the Central Universities of China(Grant No.N2304014)the Liaoning Provincial Natural Science Foundation Joint Fund(Grant No.2023-MSBA-080).
文摘1Introduction Graph partitioning is essential for large-scale distributed graph processing,as partitioning strategies directly affect graph algorithm performance[1].To ensure reliability and scalability,many graphbased applications[2](e.g.,Facebook,Weibo)deploy their services over geo-distributed datacenters(DCs),posing challenges for existing partition methods.These methods may struggle with heterogeneity(e.g.,network bandwidth)or overlook structural properties(e.g.,community structure)during optimization.
基金supported by NSFC No.62372430the Youth Innovation Promotion As-sociation CAS No.2023112.
文摘Intelligent spatial-temporal data analysis,leveraging data such as multivariate time series and geographic information,provides researchers with powerful tools to uncover multiscale patterns and enhance decision-making processes.As artificial intelligence advances,intelligent spatial-temporal algorithms have found extensive applications across various disciplines,such as geosciences,biology,and public health.1 Compared to traditional methods,these algorithms are data driven,making them well suited for addressing the complexities of modeling real-world systems.However,their reliance on substantial domain-specific expertise limits their broader applicability.Recently,significant advancements have been made in spatial-temporal large models.Trained on large-scale data,these models exhibit a vast parameter scale,superior generalization capabilities,and multitasking advantages over previous methods.Their high versatility and scalability position them as promising super hubs for multidisciplinary research,integrating knowledge,intelligent algorithms,and research communities from different fields.Nevertheless,achieving this vision will require overcoming numerous critical challenges,offering an expansive and profound space for future exploration.
