Developing a Data Model of Indoor Points of Interest to Support Location-Based Services

Alexis Richard C. Claridades, Jiyeong Lee
<span title="2020-08-01">2020</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="" style="color: black;">Journal of Sensors</a> </i> &nbsp;
Focus on indoor spatial applications has been rising with the growing interest in indoor spaces. Along with the widespread use of mobile devices and the internet, it has increased demands for indoor location-based services (LBS), demanding more efficient representation and management of indoor spatial data. Indoor points of interest (Indoor POI) data, representing both spaces and facilities located indoors, provide the infrastructure for these services. These datasets are vital in delivering
more &raquo; ... ely and accurate information to users, such as in cases of managing indoor facilities. However, even though there are studies that explore its use across applications and efforts exerted towards the standardization of the data model, most POI development studies have focused on the outdoors and remain underdeveloped in the indoors. In this paper, we propose a spatial-temporal Indoor POI data model to provide direction for the establishment of indoor POI data and to address limitations in currently available data specifications. By exploring how different Indoor POIs are from its outdoor counterparts, particularly on extending its outdoor counterparts' functions on searching, sharing, and labeling, we describe the data model and its components using the Unified Modeling Language (UML). We perform an SQL-based query experiment to demonstrate the potential use of the data model using sample data.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1155/2020/8885384</a> <a target="_blank" rel="external noopener" href="">fatcat:6ga4ni7s3jdanlgz2a2iseobxa</a> </span>
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