VIP-tree: an effective index for indoor spatial queries

Zhou Shao, Aamir Cheema, David Taniar, Hua Lu

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

15 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings of the VLDB Endowment
EditorsAlvin Cheung, Aaron Elmore
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages12
Publication statusPublished - 2016

Publication series

NameProceedings of the VLDB Endowment
PublisherAssociation for Computing Machinery (ACM)
ISSN (Electronic)2150-8097

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