Scalable Mining of Common Routes in Mobile Communication Network Traffic Data
Number of Authors: 1
2012 (English)Conference paper (Refereed)
A probabilistic method for inferring common routes from mobile communication network traffic data is presented. Besides providing mobility information, valuable in a multitude of application areas, the method has the dual purpose of enabling efficient coarse-graining as well as anonymisation by mapping individual sequences onto common routes. The approach is to represent spatial trajectories by Cell ID sequences that are grouped into routes using locality-sensitive hashing and graph clustering. The method is demonstrated to be scalable, and to accurately group sequences using an evaluation set of GPS tagged data.
Place, publisher, year, edition, pages
Computer and Information Science
IdentifiersURN: urn:nbn:se:ri:diva-24023OAI: oai:DiVA.org:ri-24023DiVA: diva2:1043102
Tenth International Conference on Pervasive Computing