Efficiently processing spatial and keyword queries in indoor venues

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

Research output: Contribution to journalArticleResearchpeer-review

3 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 first 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 are also the first to study spatial keyword queries in indoor venues. We propose a novel data structure called Keyword Partitioning Tree (KP-Tree) that indexes objects in an indoor partition. We propose an efficient algorithm based on VIP-Tree and KP-Trees to efficiently answer spatial keyword queries. Our extensive experimental study on real and synthetic data sets demonstrates that our proposed indexes outperform the existing solutions by several orders of magnitude.

Original languageEnglish
Pages (from-to)3229-3244
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number9
Publication statusPublished - 1 Sep 2021


  • Indoor index
  • Indoor query processing
  • Spatial keyword query

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