Paper Info
Title
Distributed learning of human mobility patterns from cellular network data
Abstract
The advent of ubiquitous mobile devices has provided us with an abundant spatio-temporal data source that helps us understand human mobility. The big data generated from mobile devices can be distributed at different locations and it is always infeasible to aggregate the data from multiple data collection centers into one location due to communication and privacy considerations. This paper studies human mobility patterns by learning data-adaptive representations for cellular network data that are distributed across a set of interconnected nodes. It proposes a distributed algorithm, termed cloud NN-K-SVD, for collaboratively learning a sparsifying dictionary (i.e., overcomplete basis) from the data without exchanging data samples between different nodes. The effectiveness of cloud NN-K-SVD is demonstrated through experiments on anonymized Call Detail Records from Columbus, OH.
Year
DOI
Venue
2017
10.1109/CISS.2017.7926085
2017 51st Annual Conference on Information Sciences and Systems (CISS)
Keywords
Field
DocType
distributed learning,human mobility patterns,cellular network data,ubiquitous mobile devices,spatio-temporal data source,Big Data,data collection centers,data-adaptive representations,distributed algorithm,cloud NN-K-SVD,anonymized call detail records
Computer science,Computer network,Distributed algorithm,Mobile device,Cellular network,Distributed database,Artificial neural network,Big data,Cloud computing,Encoding (memory)
Conference
ISBN
Citations 
PageRank 
978-1-5090-2697-5
0
0.34
References 
Authors
9
3
Name
Order
Citations
PageRank
Tong Wu1153.66
Raif M. Rustamov225119.58
Colin Goodall320.73