Identifying users profiles from mobile calls habits (Contributo in atti di convegno)

Type
Label
  • Identifying users profiles from mobile calls habits (Contributo in atti di convegno) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/2346496.2346500 (literal)
Alternative label
  • Furletti B., Gabrielli L., Rinzivillo S., Renso C. (2012)
    Identifying users profiles from mobile calls habits
    in ACM SIGKDD International Workshop on Urban Computing, Beijing, China, 12-16 August 2012
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Furletti B., Gabrielli L., Rinzivillo S., Renso C. (literal)
Pagina inizio
  • 17 (literal)
Pagina fine
  • 24 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Grant agreement 270833 Tipo Progetto EU_FP7 (literal)
Note
  • Scopu (literal)
  • PuMa (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; (literal)
Titolo
  • Identifying users profiles from mobile calls habits (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4503-1542-5 (literal)
Abstract
  • The huge quantity of positioning data registered by our mobile phones stimulates several research questions, mainly originating from the combination of this huge quantity of data with the extreme heterogeneity of the tracked user and the low granularity of the data. We propose a methodology to partition the users tracked by GSM phone calls into profiles like resident, commuters, in transit and tourists. The methodology analyses the phone calls with a combination of top-down and bottom up techniques where the top-down phase is based on a sequence of queries that identify some behaviors. The bottom-up is a machine learning phase to find groups of similar call behavior, thus refining the previous step. The integration of the two steps results in the partitioning of mobile traces into these four user categories that can be deeper analyzed, for example to understand the tourist movements in city or the traffic effects of commuters. An experiment on the identification of user profiles on a real dataset collecting call records from one month in the city of Pisa illustrates the methodology. (literal)
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