I. Suwardi, Dody Dharma, Dicky Prima Satya, D. Lestari
{"title":"Geohash index based spatial data model for corporate","authors":"I. Suwardi, Dody Dharma, Dicky Prima Satya, D. Lestari","doi":"10.1109/ICEEI.2015.7352548","DOIUrl":null,"url":null,"abstract":"Spatial data wrapped and processed by an application known as Geographic Information Systems (Geographical Information System / GIS). In desktop based GIS, the spatial information services only occurs when a variety of basic data has been loaded into the applications database. The development of information technology has provide ready to access, worldwide scale, web based mapping system, like Google Maps. The addition of the information content to the map can also be done easily by any user into the system. Until now, both the basic data provided by Google Maps or data that is added by users are loosely connected, meaning that there are minimum linkage between data. Thus, the data for greater benefit of a corporation or government where the data is closely related to each other still yet to be served. As spatial data being managed are voluminous, the scalability of querying performance will be a challenge. To anticipate this, we describe an improvement that built on top of our proposed spatial data model. We used a special data which derived by interleaving bits obtained from latitude-longitude pairs of a spatial data, the string called geohash. A geohash can be used as an index of every object in Spatial data table. The longer the Geohash string, the more precise the bounding box around the location it references. This approach will improve the performance of querying process of a single or even collection of spatial data in the data table of corporate GIS. The main study of this research is to provide information services along with the availability of a variety of basic spatial data owned by Google Maps. This paper highlights our recent effort in theoretical and applied research in spatial data management.","PeriodicalId":426454,"journal":{"name":"2015 International Conference on Electrical Engineering and Informatics (ICEEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Electrical Engineering and Informatics (ICEEI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEI.2015.7352548","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19
Abstract
Spatial data wrapped and processed by an application known as Geographic Information Systems (Geographical Information System / GIS). In desktop based GIS, the spatial information services only occurs when a variety of basic data has been loaded into the applications database. The development of information technology has provide ready to access, worldwide scale, web based mapping system, like Google Maps. The addition of the information content to the map can also be done easily by any user into the system. Until now, both the basic data provided by Google Maps or data that is added by users are loosely connected, meaning that there are minimum linkage between data. Thus, the data for greater benefit of a corporation or government where the data is closely related to each other still yet to be served. As spatial data being managed are voluminous, the scalability of querying performance will be a challenge. To anticipate this, we describe an improvement that built on top of our proposed spatial data model. We used a special data which derived by interleaving bits obtained from latitude-longitude pairs of a spatial data, the string called geohash. A geohash can be used as an index of every object in Spatial data table. The longer the Geohash string, the more precise the bounding box around the location it references. This approach will improve the performance of querying process of a single or even collection of spatial data in the data table of corporate GIS. The main study of this research is to provide information services along with the availability of a variety of basic spatial data owned by Google Maps. This paper highlights our recent effort in theoretical and applied research in spatial data management.
由地理信息系统(Geographic Information System / GIS)应用程序包装和处理的空间数据。在基于桌面的地理信息系统中,只有将各种基础数据加载到应用程序数据库中,才能提供空间信息服务。信息技术的发展已经提供了随时可以访问的、全球规模的、基于网络的地图系统,比如谷歌地图。向地图添加信息内容也可以由系统中的任何用户轻松完成。到目前为止,无论是谷歌地图提供的基础数据,还是用户自己添加的数据,都是松散连接的,数据之间的联系是最小的。因此,为企业或政府提供更大利益的数据,其中的数据彼此密切相关,仍有待服务。由于所管理的空间数据非常庞大,查询性能的可伸缩性将是一个挑战。为了预测这一点,我们描述了建立在我们提出的空间数据模型之上的改进。我们使用了一种特殊的数据,它是通过从空间数据的经纬度对中获得的交错位派生的,称为geohash的字符串。geohash可以作为空间数据表中每个对象的索引。Geohash字符串越长,它所引用位置周围的边界框就越精确。该方法将提高企业GIS数据表中单个甚至一组空间数据查询过程的性能。本研究的主要研究内容是提供信息服务,以及谷歌地图所拥有的各种基础空间数据的可用性。本文重点介绍了近年来我国在空间数据管理方面的理论和应用研究。