Cleansing and Analytics of Indoor Positioning Data

Xiao Li*

*Corresponding author

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

Abstract

Indoor positioning data is of high significance to many indoor location-based services whereas it is of low quality due to the limitations of indoor positioning technologies. Thus, our work is focused on cleansing indoor positioning data to enhance its quality significantly. In this paper, we first introduce the data quality issues consisting in indoor positioning data and propose a cleansing framework to handle such issues. Subsequently, we formulate four specific research questions in order to settle related quality issues. In addition, we present promising methodologies and comprehensive evaluation criteria to resolve our proposed research questions.

OriginalsprogEngelsk
TitelProceedings - 2022 23rd IEEE International Conference on Mobile Data Management, MDM 2022
Antal sider3
ForlagIEEE
Publikationsdato2022
Sider334-336
ISBN (Elektronisk)9781665451765
DOI
StatusUdgivet - 2022
Begivenhed23rd IEEE International Conference on Mobile Data Management, MDM 2022 - Virtual, Paphos, Cypern
Varighed: 6 jun. 20229 jun. 2022

Konference

Konference23rd IEEE International Conference on Mobile Data Management, MDM 2022
Land/OmrådeCypern
ByVirtual, Paphos
Periode06/06/202209/06/2022
NavnProceedings - IEEE International Conference on Mobile Data Management
Vol/bind2022-June
ISSN1551-6245

Bibliografisk note

Funding Information:
VI. ACKNOWLEDGEMENT This work is supported by Independent Research Fund Denmark (No. 8022-00366B), and Department of People and Technology, Roskilde University. In addition, I would like to appreciate my supervisor Professor Hua Lu for his patient support and professional guidance to my PhD study. Also, I would like to thank Assistant Professor Huan Li for his assistance and guidance in my work.

Publisher Copyright:
© 2022 IEEE.

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