VIP-Tree: An Effective Index for Indoor Spatial Queries

Zhou Shao, Muhammad Aamir Cheema, David Taniar, Hua Lu

Publikation: Bidrag til tidsskriftKonferenceartikelForskningpeer review


Due to the growing popularity of indoor location-based services, indoor data management has received significant research attention in the past few years. However, we observe that the existing indexing and query processing techniques for the indoor space do not fully exploit the properties of the indoor space. Consequently, they provide below par performance which makes them unsuitable for large indoor venues with high query workloads. In this paper, we propose two novel indexes called Indoor Partitioning Tree (IP-Tree) and Vivid IP-Tree (VIP-Tree) that are carefully designed by utilizing the properties of indoor venues. The proposed indexes are lightweight, have small pre-processing cost and provide near-optimal performance for shortest distance and shortest path queries. We also present efficient algorithms for other spatial queries such as k nearest neighbors queries and range queries. Our extensive experimental study on real and synthetic data sets demonstrates that our proposed indexes outperform the existing algorithms by several orders of magnitude.
TidsskriftProceedings of the VLDB Endowment
Udgave nummer4
Sider (fra-til)325-336
Antal sider12
StatusUdgivet - 2016
Udgivet eksterntJa
Begivenhed43rd International Conference on Very Large Data Bases - Technical University Munich, München, Tyskland
Varighed: 28 aug. 20161 sep. 2016


Konference43rd International Conference on Very Large Data Bases
LokationTechnical University Munich

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