{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:27:03Z","timestamp":1722472023663},"reference-count":59,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T00:00:00Z","timestamp":1655856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19060103"]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671401"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Many effective and advanced methods have been developed to explore oceanic dynamics using time series of raster-formatted datasets; however, they have generally been designed at a scale suitable for data observation and used independently of each other, despite the potential advantages of combining different modules into an integrated system at a scale suited for dynamic evolution. From raster-formatted datasets to marine knowledge, we developed and integrated several mining algorithms at a dynamic evolutionary scale and combined them into six modules: a module of raster-formatted dataset pretreatment; a module of process-oriented object extraction; a module of process-oriented representation and management (process-oriented graph database); a module of process-oriented clustering; a module of process-oriented association rule mining; and a module of process-oriented visualization. On the basis of such modules, we developed a process-oriented spatiotemporal dynamic mining system named PoSDMS (Process-oriented Spatiotemporal Dynamics Mining System). PoSDMS was designed to have the capacity to deal with at least six environments of marine anomalies with 40 years of raster-formatted datasets, including their extraction, representation, storage, clustering, association and visualization. The effectiveness of the integrated system was evaluated in a case study of sea surface temperature datasets during the period from January 1982 to December 2021 in global oceans. The main contribution of this study was the development of a mining system at a scale suited for dynamic evolution, providing an analyzing platform or tool to deal with time series of raster-formatted datasets to aid in obtaining marine knowledge.<\/jats:p>","DOI":"10.3390\/rs14132991","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T03:11:19Z","timestamp":1655953879000},"page":"2991","source":"Crossref","is-referenced-by-count":0,"title":["PoSDMS: A Mining System for Oceanic Dynamics with Time Series of Raster-Formatted Datasets"],"prefix":"10.3390","volume":"14","author":[{"given":"Lianwei","family":"Li","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3605-6578","authenticated-orcid":false,"given":"Cunjin","family":"Xue","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Yangfeng","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5713-4825","authenticated-orcid":false,"given":"Chengbin","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"State Key Laboratory of Resource and Environmental Information System, Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Chaoran","family":"Niu","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yuan, M., Mark, D.M., Egenhofer, M.J., and Peuquet, D.J. 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