{"title":"基于聚类的只读数据库空间分析索引","authors":"Chiu-Wing Sham, K. Cheung, Chung Yim Edward Yiu","doi":"10.1109/ICASI57738.2023.10179500","DOIUrl":null,"url":null,"abstract":"The one-time analysis is a widely-used method in spatial analysis, as it allows researchers to obtain raw data from a database and use a computer program to reconstruct a one-time database for analysis. However, this process can be resource-intensive, as the spatial database is typically read-only and requires the use of a complex data structure. In this paper, we propose the use of a cluster-based index (CBI) as a solution to these issues. CBIs are a type of indexing technique that can be used to improve the efficiency of spatial queries in read-only databases. By using a CBI, we can reduce the overhead of computations and significantly improve the execution time of spatial analysis.","PeriodicalId":281254,"journal":{"name":"2023 9th International Conference on Applied System Innovation (ICASI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cluster based Indexing for Spatial Analysis on Read-only Database\",\"authors\":\"Chiu-Wing Sham, K. Cheung, Chung Yim Edward Yiu\",\"doi\":\"10.1109/ICASI57738.2023.10179500\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The one-time analysis is a widely-used method in spatial analysis, as it allows researchers to obtain raw data from a database and use a computer program to reconstruct a one-time database for analysis. However, this process can be resource-intensive, as the spatial database is typically read-only and requires the use of a complex data structure. In this paper, we propose the use of a cluster-based index (CBI) as a solution to these issues. CBIs are a type of indexing technique that can be used to improve the efficiency of spatial queries in read-only databases. By using a CBI, we can reduce the overhead of computations and significantly improve the execution time of spatial analysis.\",\"PeriodicalId\":281254,\"journal\":{\"name\":\"2023 9th International Conference on Applied System Innovation (ICASI)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 9th International Conference on Applied System Innovation (ICASI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASI57738.2023.10179500\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 9th International Conference on Applied System Innovation (ICASI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASI57738.2023.10179500","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cluster based Indexing for Spatial Analysis on Read-only Database
The one-time analysis is a widely-used method in spatial analysis, as it allows researchers to obtain raw data from a database and use a computer program to reconstruct a one-time database for analysis. However, this process can be resource-intensive, as the spatial database is typically read-only and requires the use of a complex data structure. In this paper, we propose the use of a cluster-based index (CBI) as a solution to these issues. CBIs are a type of indexing technique that can be used to improve the efficiency of spatial queries in read-only databases. By using a CBI, we can reduce the overhead of computations and significantly improve the execution time of spatial analysis.