Contributo in atti di convegno, 2012, ENG
Coscia M., Rinzivillo S., Giannotti F., Pedreschi D.
CNR-ISTI, Pisa; CNR-ISTI, Pisa; CNR-ISTI, Pisa; Department of Computer Science, University of Pisa, Pisa, Italy
The availability of massive network and mobility data from diverse domains has fostered the analysis of human be- haviors and interactions. This data availability leads to challenges in the knowledge discovery community. Several different analyses have been performed on the traces of human trajectories, such as understanding the real borders of human mobility or mining social interactions derived from mobility and viceversa. However, the data quality of the digital traces of human mobility has a dramatic impact over the knowledge that it is possible to mine, and this issue has not been thoroughly tackled so far in literature. In this paper, we mine and analyze with complex network techniques a large dataset of human trajectories, a GPS dataset from more than 150k vehicles in Italy. We build a multiresolution grid and we map the trajectories with several complex networks, by connecting the different areas of our region of interest. Then we analyze the structural properties of these networks and the quality of the borders it is possible to infer from them. The result is a significant advancement in our understanding of the data transformation process that is needed to connect mobility with social network analysis and mining.
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 248–252, Instanbul, Turkey, 26-29 August 2012
Community Discovery, Mobility data analysis, Database Applications
Coscia Michele, Giannotti Fosca, Rinzivillo Salvatore
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 276078
Year: 2012
Type: Contributo in atti di convegno
Creation: 2014-01-09 12:29:19.000
Last update: 2014-05-13 09:28:53.000
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6425755
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:276078
Scopus: 2-s2.0-84874283811
ISI Web of Science (WOS): 000320443500036
PUMA: cnr.isti/2012-A2-081