01393nas a2200169   4500008004100000245005700041210005700098260002000155520084000175100002201015700002301037700002201060700002201082700002101104700002201125856007601147       2015                        eng d00aDetecting and understanding big events in big cities0 aDetecting and understanding big events in big cities  aBostonc04/20153 aRecent studies have shown the great potential of big data such as mobile phone location data to model human behavior. Big data allow to analyze people presence in a territory in a fast and effective way with respect to the classical surveys (diaries or questionnaires). One of the drawbacks of these collection systems is incompleteness of the users' traces; people are localized only when they are using their phones. In this work we define a data mining method for identifying people presence and understanding the impact of big events in big cities. We exploit the ability of the Sociometer for classifying mobile phone users in mobility categories through their presence profile. The experiment in cooperation with Orange Telecom has been conduced in Paris during the event F^ete de la Musique using a
privacy preserving protocol.1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aTrasarti, Roberto1 aSmoreda, Zbigniew1 aVanhoof, Maarten1 aZiemlicki, Cezary  uhttp://www.netmob.org/assets/img/netmob15_book_of_abstracts_posters.pdf01871nas a2200217   4500008003900000022001400039245010000053210006900153300000600222520115600228653001901384100002201403700003101425700001701456700002201473700002201495700002101517700002201538700002201560856007101582       2014                          d  a0308-596100aDiscovering urban and country dynamics from mobile phone data with spatial correlation patterns0 aDiscovering urban and country dynamics from mobile phone data wi  a-3 aAbstract Mobile communication technologies pervade our society and existing wireless networks are able to sense the movement of people, generating large volumes of data related to human activities, such as mobile phone call records. At the present, this kind of data is collected and stored by telecom operators infrastructures mainly for billing reasons, yet it represents a major source of information in the study of human mobility. In this paper, we propose an analytical process aimed at extracting interconnections between different areas of the city that emerge from highly correlated temporal variations of population local densities. To accomplish this objective, we propose a process based on two analytical tools: (i) a method to estimate the presence of people in different geographical areas; and (ii) a method to extract time- and space-constrained sequential patterns capable to capture correlations among geographical areas in terms of significant co-variations of the estimated presence. The methods are presented and combined in order to deal with two real scenarios of different spatial scale: the Paris Region and the whole France.10aUrban dynamics1 aTrasarti, Roberto1 aOlteanu-Raimond, Ana-Maria1 aNanni, Mirco1 aCouronné, Thomas1 aFurletti, Barbara1 aGiannotti, Fosca1 aSmoreda, Zbigniew1 aZiemlicki, Cezary  uhttp://www.sciencedirect.com/science/article/pii/S0308596113002012