{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:05:06Z","timestamp":1760148306652,"version":"build-2065373602"},"reference-count":86,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T00:00:00Z","timestamp":1682035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41601484","41830645","2017YFB0503604","2019YFE0106500","220100056"],"award-info":[{"award-number":["41601484","41830645","2017YFB0503604","2019YFE0106500","220100056"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["41601484","41830645","2017YFB0503604","2019YFE0106500","220100056"],"award-info":[{"award-number":["41601484","41830645","2017YFB0503604","2019YFE0106500","220100056"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Fund of Hubei Luojia Laboratory","award":["41601484","41830645","2017YFB0503604","2019YFE0106500","220100056"],"award-info":[{"award-number":["41601484","41830645","2017YFB0503604","2019YFE0106500","220100056"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Similar time series search is one of the most important time series mining tasks in our daily life. As recent advances in sensor technologies accumulate abundant multi-dimensional time series data associated with multivariate quantities, it becomes a privilege to adapt similar time series searches for large-scale and multi-dimensional time series data. However, traditional similar time series search methods are mainly designed for one-dimensional time series, while advanced methods applicable for multi-dimensional time series data are largely immature and, more importantly, are not friendly to users from the domain of geography. As an alternative, we propose a novel method to search similar multi-dimensional time series with spatial databases. Compared with traditional methods that often conduct the similarity search based on features of the raw time series data sequence, the proposed method stores multi-dimensional time series as spatial objects in a spatial database, and then searches similar time series based on their spatial features. To demonstrate the validity of the proposed method, we analyzed the correlation between temporal features of the raw time series and spatial features of their corresponding spatial objects theoretically and empirically. Results indicate that the proposed method can not only support similar multi-dimensional time series searches but also markedly improve its efficiency under many specific scenarios. We believe that such a new paradigm will shed further light on the similarity search in large-scale multi-dimensional time series data, and will lower the barrier for users familiar with spatial databases to conduct complex time series mining tasks.<\/jats:p>","DOI":"10.3390\/ijgi12040179","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T10:11:25Z","timestamp":1682071885000},"page":"179","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Querying Similar Multi-Dimensional Time Series with a Spatial Database"],"prefix":"10.3390","volume":"12","author":[{"given":"Zheren","family":"Liu","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0122-9419","authenticated-orcid":false,"given":"Chaogui","family":"Kang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, 68 Jincheng Street, Wuhan 430078, China"},{"name":"Hubei Luojia Laboratory, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyue","family":"Xing","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information Systems, School of Earth and Space Sciences, Peking University, 5 Yiheyuan Road, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.compenvurbsys.2012.02.002","article-title":"Pervasive location acquisition technologies: Opportunities and challenges for geospatial studies","volume":"36","author":"Lu","year":"2012","journal-title":"Comput. 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