Towards the integration of indoorGML and indoorlocationGML for indoor applications
L. Liu (TU Delft - Urban Data Science)
S. Zlatanova (TU Delft - Urban Data Science)
Q. Zhu (Southwest Jiaotong University)
K. Li (Pusan National University)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
This paper introduces and compares two types of GML-based data standards for indoor location-based services, i.e., iIndoorGML and iIndoorLocationGML. By elaborating the advantages of the both standards and their data models, we conclude that the two data standards are complementary to each other. A jointed data model is presented to show the integration of the two standards. iIndoorGML can supply subdivision of building for data of iIndoorLocationGML, and the semantics of locations defined in iIndoorLocationGML can be added to iIndoorGML. By proposing two use cases, we take the initiative in attempting to combine the use of the two standards. The first case is to collect details from files of the two standards for an indoor path; the second one is to generate verbal directions for indoor guidance from files of the two standards. Some future work is given for further development, such as automatic integration of separate data from both standards.
help_outline