Data Management Foundations for Indoor LBS


The purpose of the project is to create a prototype indoor data management system with general, systematic, and solid data management foundations upon which indoor location-based services (LBS) can be built to provide location-dependent information efficiently and effectively to users in a broad range of indoor scenarios.

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indoor LBS has yet to be widely available in practice


A fundamental reason for the gap is that indoor data management is still in its infancy. The recent trend of pervasive instrumentation is improving indoor positioning and accumulating large amounts of real data, which inspires research and applications on indoor data management and enables us to evaluate relevant innovations in real settings—an opportunity for research that we have never had before.

See our efforts!

The Team Members


Hua Lu (PI)

Professor of Computer Science in the Department of People and Technology, Roskilde University.

Huan Li

Assistant Professor in the Department of Computer Science, Aalborg University.

Harry Kai-Ho Chan

Postdoc Researcher in the Department of People and Technology, Roskilde University.

Tiantian Liu

PhD student in the Department of Computer Science, Aalborg University.

Xiao Li

PhD student in the Department of People and Technology, Roskilde University.

Publications


  • Time-Constrained Indoor Keyword-aware Routing.
    Harry Kai-Ho Chan, Tiantian Liu, Huan Li, and Hua Lu.
    Proceedings of the 17th International Symposium on Spatial and Temporal Databases (SSTD), pp. 74-84, 2021.
    FULLTEXT
  • Towards indoor temporal-variation aware shortest path query.
    Tiantian Liu, Zijin Feng, Huan Li, Hua Lu, Muhammad Aamir Cheema, Hong Cheng, and Jianliang Xu.
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 15 pages, 2021.
    PREPRINT †
  • Towards crowd-aware indoor path planning.
    Tiantian Liu, Huan Li, Hua Lu, Muhammad Aamir Cheema, and Lidan Shou.
    Proceedings of the 47th International Conference on Very Large Data Bases (PVLDB), 14(8): 1365--1377, 2021.
    FULL TEXT
    EXTENSION
  • Indoor spatial queries: Modeling, indexing, and processing.
    Tiantian Liu, Huan Li, Hua Lu, Muhammad Aamir Cheema, and Lidan Shou.
    Proceedings of the 24th International Conference on Extending Database Technology (EDBT), pp. 181-192, 2021.
    FULL TEXT
    1-MINUTE POSTER
    SLIDES
    EXTENSION
    CODE&DATA
  • Toward translating raw indoor positioning data into mobility semantics.
    Huan Li, Hua Lu, Gang Chen, Ke Chen, Qinkuang Chen, and Lidan Shou.
    ACM/IMS Transactions on Data Science (TDS), 1(4) No.26: 1-37, 2020.
    FULL TEXT
  • Efficiently processing spatial and keyword queries in indoor venues.
    Zhou Shao, Muhammad Aamir Cheema, David Taniar, Hua Lu, and Shiyu Yang.
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 14 pages, 2020.
    FULL TEXT
  • Shortest path queries for indoor venues with temporal variations.
    Tiantian Liu, Zijin Feng, Huan Li, Hua Lu, Muhammad Aamir Cheema, Hong Cheng, and Jianliang Xu.
    Proceedings of the 36th IEEE International Conference on Data Engineering (ICDE), pp. 2014-2017, 2020. (Short Paper)
    PREPRINT †
    POSTER
  • Indoor mobility semantics annotation using coupled conditional Markov networks.
    Huan Li, Hua Lu, Muhammad Aamir Cheema, Lidan Shou, and Gang Chen.
    Proceedings of the 36th IEEE International Conference on Data Engineering (ICDE), pp. 1441-1452, 2020.
    PREPRINT †
    SLIDES
  • Indoor top-k keyword-aware routing query.
    Zijin Feng, Tiantian Liu, Huan Li, Hua Lu, Lidan Shou, and Jianliang Xu.
    Proceedings of the 36th IEEE International Conference on Data Engineering (ICDE), pp. 1213-1224, 2020.
    PREPRINT †
    SLIDES
    POSTER
    DATA


preprint version under OpenAccess protocol

Contact



The project was supported by Independent Research Fund Denmark (Grant No. 8022-00366B).
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