Abstract
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 timely 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.
Highlights
Nowadays, day-to-day human activities have been closely tied with the use of mobile devices and gadgets, most equipped with GPS receivers and cameras, and are continuously improving in terms of features and speeds while decreasing in size [1, 2]
The demand for information arose through location-based services (LBS), which aim to give users relevant and timely information based on their positions [1, 3], and augmented reality (AR) applications that combine images from the real-world to virtual images in three-dimensions [4]
In response to the difficulties faced in dealing with Indoor POI stated above, this study proposes a data model that characterizes its vital aspects as essential elements in providing spatial services
Summary
Day-to-day human activities have been closely tied with the use of mobile devices and gadgets, most equipped with GPS receivers and cameras, and are continuously improving in terms of features and speeds while decreasing in size [1, 2]. A reliable Indoor POI dataset is vital to provide the fundamental infrastructure to LBS, to provide successful services to users This approach, is faced with several difficulties. In response to the difficulties faced in dealing with Indoor POI stated above, this study proposes a data model that characterizes its vital aspects as essential elements in providing spatial services. Identifying these aspects and formalizing this model is key for assuring data quality, provide prospects for validation, encourage analysis, and at the same time, promoting data sharing and integration. We conduct an experimental implementation of the data model through a use case involving facility management to demonstrate its various aspects through a small sample dataset, and the last section focuses on conclusions and limitations of this study to be addressed by future work
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