Contextual Location Imputation for Confined WiFi Trajectories
Elham Naghizade, Jeffrey Chan, Yongli Ren, Martin Tomko, D Phung (ed.), VS Tseng (ed.), GI Webb (ed.), B Ho (ed.), M Ganji (ed.), L Rashidi (ed.)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | SPRINGER INTERNATIONAL PUBLISHING AG | Published : 2018
The analysis of mobility patterns from large-scale spatio-temporal datasets is key to personalised location-based applications. Datasets capturing user location are, however, often incomplete due to temporary failures of sensors, deliberate interruptions or because of data privacy restrictions. Effective location imputation is thus a critical processing step enabling mobility pattern mining from sparse data. To date, most studies in this area have focused on coarse location prediction at city scale. In this paper we aim to infer the missing location information of individuals tracked within structured, mostly confined spaces such as a university campus or a mall. Many indoor tracking dataset..View full abstract
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Awarded by Linkage Project grant of the Australian Research Council
Awarded by Discovery Project grant of the Australian Research Council
This research is supported by a Linkage Project grant of the Australian Research Council (LP120200413) and a Discovery Project grant of the Australian Research Council (DP170100153).