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Owing to the advance of wireless network and mobile devices, location-based services (LBSs) play more and more important roles in our daily life. Location modeling is an important research topic in LBSs, which is essential to the development of a well-defined representation of location knowledge for location browsing, navigation and query processing. In this paper, we apply the multidimensional scaling (MDS) technique to build a 2-dimensional embedded indoor location model, where the actual distances between all pairs of points in the original location area are preserved. We propose efficient algorithms to answer KNN and shortest path queries on the original area using only the simple embedded location model. Detailed examples are given to demonstrate the ideas and efficiency of our model.

Owing to the advance of wireless network and mobile devices, location-based services (LBSs) play more and more important roles in our daily life. Location modeling is an important research topic in LBSs, which is essential to the development of a well-defined representation of location knowledge for location browsing, navigation and query processing. In this paper, we apply the multidimensional scaling (MDS) technique to build a 2-dimensional embedded indoor location model, where the actual distances between all pairs of points in the original location area are preserved. We propose efficient algorithms to answer KNN and shortest path queries on the original area using only the simple embedded location model. Detailed examples are given to demonstrate the ideas and efficiency of our model.

Owing to the advance of wireless network and mobile devices, location-based services (LBSs) play more and more important roles in our daily life. Location modeling is an important research topic in LBSs, which is essential to the development of a well-defined representation of location knowledge for location browsing, navigation and query processing. In this paper, we apply the multidimensional scaling (MDS) technique to build a 2-dimensional embedded indoor location model, where the actual distances between all pairs of points in the original location area are preserved. We propose efficient algorithms to answer KNN and shortest path queries on the original area using only the simple embedded location model. Detailed examples are given to demonstrate the ideas and efficiency of our model.