Computer Science ›› 2017, Vol. 44 ›› Issue (8): 187-192.doi: 10.11896/j.issn.1002-137X.2017.08.033

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Context-dependent Double-layered Data Model for Indoor Space

LI Jing-wen, LIU Yu-lei and QIN Xiao-lin   

  • Online:2018-11-13 Published:2018-11-13

Abstract: In the management of moving objects for indoor space,how to build the data model is the most important problem to be solved.With the development of context-aware information system,the concept of the context is getting more and more attention.How to integrate the context and preference information into the indoor spatial data management has become the focus of attention.Aiming at this problem,considering three kinds of information,such as geometry,topology and context,a context-dependent double-layered data model for indoor space was built.After analyzing the classical methods of space partition, we introduced a new method called the fine-grained partition for indoor space and gave its formal definition.We adopted the idea of hierarchical complementary to organize the indoor space,and the context information was added to the model by using ontology,which provides a flexible representation for indoor space.Finally,the feasibility and validity of the modeling method are illustrated by the examples and the advantages of this model.

Key words: Context,Indoor space,Data model,Context ontology

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