{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T05:25:09Z","timestamp":1740115509988,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>Data organization, storage, management, analysis and mining are the core of wisdom in smart city. With the development of big data technology and the promotion of smart city construction, how to manage more massive geographic information data has become a hot topic of research. This paper designs a GIS data management system based on distributed database HBase. The system optimizes the generation and storage process of raster data and writes massive raster data to HBase storage and index directly. At the same time, in view of the storage, index and retrieval of vector spatial data, a new design of rowkey is proposed. Considering both the latitude and longitude, and the type and attribute of the spatial data, it can quickly locate the data to be returned through the rowkey of HBase when the vector geographic information is retrieved in space position, and the data will be carried out. Fast filtering can achieve the purpose of retrieving vector spatial data at high speed. The above methods are verified with real GIS data in the cluster environment of HBase. The results show that the proposed system has high data storage and retrieval performance and achieves high efficiency when perform storage and real-time high-speed retrieval of massive geographic information data.<\/jats:p>","DOI":"10.3233\/978-1-61499-927-0-787","type":"book-chapter","created":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T12:06:08Z","timestamp":1740053168000},"source":"Crossref","is-referenced-by-count":0,"title":["A Distributed Management System for Massive GIS Data Using HBase"],"prefix":"10.3233","author":[{"family":"Liu Dawei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Li Xuemei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Wang Haiyang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining IV"],"original-title":[],"deposited":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T12:15:17Z","timestamp":1740053717000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-926-3&spage=787&doi=10.3233\/978-1-61499-927-0-787"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-927-0-787","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2018]]}}}