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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F10%3A00172398%21RIV11-MSM-21230___
rdf:type
skos:Concept n13:Vysledek
dcterms:description
Data captured from a live cellular network with the real users during their common daily routine help to understand how the users move within the network. Unlike the simulations with limited potential or expensive experimental studies, the research in user-mobility or spatio-temporal user behavior can be conducted on publicly available datasets such as the Reality Mining Dataset. These data have been for many years a source of valuable information about social interconnection between users and user-network associations. However, an important, spatial dimension is missing in this dataset. In this paper, we present a methodology for retrieving geographical locations matching the GSM cell identifiers in the Reality Mining Dataset, an approach base on querying the Google Location API. A statistical analysis of the measure of success of locations retrieval is provided. Further, we present the LAC-clustering method for detecting and removing outliers. Data captured from a live cellular network with the real users during their common daily routine help to understand how the users move within the network. Unlike the simulations with limited potential or expensive experimental studies, the research in user-mobility or spatio-temporal user behavior can be conducted on publicly available datasets such as the Reality Mining Dataset. These data have been for many years a source of valuable information about social interconnection between users and user-network associations. However, an important, spatial dimension is missing in this dataset. In this paper, we present a methodology for retrieving geographical locations matching the GSM cell identifiers in the Reality Mining Dataset, an approach base on querying the Google Location API. A statistical analysis of the measure of success of locations retrieval is provided. Further, we present the LAC-clustering method for detecting and removing outliers.
dcterms:title
Spatial Extension of the Reality Mining Dataset Spatial Extension of the Reality Mining Dataset
skos:prefLabel
Spatial Extension of the Reality Mining Dataset Spatial Extension of the Reality Mining Dataset
skos:notation
RIV/68407700:21230/10:00172398!RIV11-MSM-21230___
n4:aktivita
n9:S
n4:aktivity
S
n4:dodaniDat
n6:2011
n4:domaciTvurceVysledku
n11:8275432 n11:3803864
n4:druhVysledku
n19:D
n4:duvernostUdaju
n14:S
n4:entitaPredkladatele
n20:predkladatel
n4:idSjednocenehoVysledku
288966
n4:idVysledku
RIV/68407700:21230/10:00172398
n4:jazykVysledku
n5:eng
n4:klicovaSlova
mobility; tracking; Reality Mining; GSM; Cell-ID; agglomerative clustering
n4:klicoveSlovo
n8:mobility n8:GSM n8:Reality%20Mining n8:agglomerative%20clustering n8:Cell-ID n8:tracking
n4:kontrolniKodProRIV
[09AA37229C36]
n4:mistoKonaniAkce
San Francisco, CA
n4:mistoVydani
New York
n4:nazevZdroje
The 7th IEEE International Conference on Mobile Ad-hoc and Sensor Systems
n4:obor
n10:IN
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:rokUplatneniVysledku
n6:2010
n4:tvurceVysledku
Kencl, Lukáš Ficek, Michal
n4:typAkce
n18:WRD
n4:zahajeniAkce
2010-11-08+01:00
s:numberOfPages
8
n16:hasPublisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
n12:isbn
978-1-4244-7489-9
n15:organizacniJednotka
21230