Indoor top-k keyword-aware routing query

Zijin Feng, Tiantian Liu, Huan Li, Hua Lu, Lidan Shou, Jianliang Xu

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

24 Citations (Scopus)


People have many activities indoors and there is an increasing demand of keyword-aware route planning for indoor venues. In this paper, we study the indoor top-k keyword-aware routing query (IKRQ). Given two indoor points s and t, an IKRQ returns k s-to-t routes that do not exceed a given distance constraint but have optimal ranking scores integrating keyword relevance and spatial distance. It is challenging to efficiently compute the ranking scores and find the best yet diverse routes in a large indoor space with complex topology. We propose prime routes to diversify top-k routes, devise mapping structures to organize indoor keywords and compute route keyword relevances, and derive pruning rules to reduce search space in routing. With these techniques, we design two search algorithms with different routing expansions. Experiments on synthetic and real data demonstrate the efficiency of our proposals.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
PublisherIEEE Computer Society
Number of pages12
ISBN (Electronic)9781728129037
Publication statusPublished - Apr 2020
Event36th IEEE International Conference on Data Engineering, ICDE 2020 - Dallas, United States
Duration: 20 Apr 202024 Apr 2020 (Link to conference proceedings)

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Conference36th IEEE International Conference on Data Engineering, ICDE 2020
Country/TerritoryUnited States
Internet address

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems


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