2013
Conference article
Unknown
Validating general human mobility patterns on massive GPS data
Pappalardo L., Rinzivillo S., Pedreschi D., Giannotti F.
Are the patterns of car travel dierent from those of general human mobility? Based on a unique dataset consisting of the GPS tra- jectories of 10 million travels accomplished by 150,000 cars in Italy, we investigate how known mobility models apply to car travels, and illus- trate novel analytical ndings. We also assess to what extent the sample in our dataset is representative of the overall car mobility, and discover how to build an extremely accurate model that, given our GPS data, estimates the real trac values as measured by road sensors.Source: SEBD 2013 - 21st Italian Symposium on Advanced Database Systems, pp. 305–312, Università di Reggio Calabria, 30 June - 03 July 2013
Pappalardo L., Rinzivillo S., Pedreschi D., Giannotti F.
Are the patterns of car travel dierent from those of general human mobility? Based on a unique dataset consisting of the GPS tra- jectories of 10 million travels accomplished by 150,000 cars in Italy, we investigate how known mobility models apply to car travels, and illus- trate novel analytical ndings. We also assess to what extent the sample in our dataset is representative of the overall car mobility, and discover how to build an extremely accurate model that, given our GPS data, estimates the real trac values as measured by road sensors.Source: SEBD 2013 - 21st Italian Symposium on Advanced Database Systems, pp. 305–312, Università di Reggio Calabria, 30 June - 03 July 2013
See at: CNR ExploRA
2013
Journal article
Closed Access
Understanding the patterns of car travel
Pappalardo L., Rinzivillo S., Qu Z., Pedreschi D., Giannotti F.
Are the patterns of car travel different from those of general human mobility? Based on a unique dataset consisting of the GPS trajectories of 10 million travels accomplished by 150,000 cars in Italy, we investigate how known mobility models apply to car travels, and illustrate novel analytical findings. We also assess to what extent the sample in our dataset is representative of the overall car mobility, and discover how to build an extremely accurate model that, given our GPS data, estimates the real traffic values as measured by road sensors.Source: The European physical journal. Special topics 215 (2013): 61–73. doi:10.1140/epjst/e2013-01715-5
DOI: 10.1140/epjst/e2013-01715-5
Metrics:
Pappalardo L., Rinzivillo S., Qu Z., Pedreschi D., Giannotti F.
Are the patterns of car travel different from those of general human mobility? Based on a unique dataset consisting of the GPS trajectories of 10 million travels accomplished by 150,000 cars in Italy, we investigate how known mobility models apply to car travels, and illustrate novel analytical findings. We also assess to what extent the sample in our dataset is representative of the overall car mobility, and discover how to build an extremely accurate model that, given our GPS data, estimates the real traffic values as measured by road sensors.Source: The European physical journal. Special topics 215 (2013): 61–73. doi:10.1140/epjst/e2013-01715-5
DOI: 10.1140/epjst/e2013-01715-5
Metrics:
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The European Physical Journal Special Topics 
| link.springer.com | CNR ExploRA
2013
Contribution to conference
Restricted
The origin of human heterogeneity: analyzing mobility behavior through GSM and GPS data
Pappalardo L., Simini F., Rinzivillo S., Pedreschi D., Giannotti F.
In the present work, we present a macro-micro analysis of human mobility conducted on two dierent datasets. The rst one is a GSM dataset collected by a European mobile phone carrier for billing and operational purposes. It contains date, time and coordinates of the phone tower routing the communication for each call and text message sent or received by 91; 000 customers, in a period of 3 months. The other one is a GPS dataset storing information of approximately 9; 8 Million dierent car travels from 159; 000 cars tracked during one month (May 2011) in an area corresponding to central Italy.Source: ECCS 2013 - European Conference on Complex Systems, Barcellona, Spain, 16-20 September 2013
Pappalardo L., Simini F., Rinzivillo S., Pedreschi D., Giannotti F.
In the present work, we present a macro-micro analysis of human mobility conducted on two dierent datasets. The rst one is a GSM dataset collected by a European mobile phone carrier for billing and operational purposes. It contains date, time and coordinates of the phone tower routing the communication for each call and text message sent or received by 91; 000 customers, in a period of 3 months. The other one is a GPS dataset storing information of approximately 9; 8 Million dierent car travels from 159; 000 cars tracked during one month (May 2011) in an area corresponding to central Italy.Source: ECCS 2013 - European Conference on Complex Systems, Barcellona, Spain, 16-20 September 2013
See at:
www.eccs13.eu | CNR ExploRA
2013
Conference article
Open Access
The three dimensions of social prominence
Pennacchioli D., Rossetti G., Pappalardo L., Pedreschi D., Giannotti F., Coscia M.
One classic problem denition in social network analysis is the study of diusion in networks, which enables us to tackle problems like favoring the adoption of positive technologies. Most of the attention has been turned to how to maximize the number of in uenced nodes, but this approach misses the fact that dierent scenarios imply dierent dif- fusion dynamics, only slightly related to maximizing the number of nodes involved. In this paper we measure three dierent dimensions of social prominence: the Width, i.e. the ratio of neighbors in uenced by a node; the Depth, i.e. the degrees of separation from a node to the nodes perceiv- ing its prominence; and the Strength, i.e. the intensity of the prominence of a node. By dening a procedure to extract prominent users in complex networks, we detect associations between the three dimensions of social prominence and classical network statistics. We validate our results on a social network extracted from the Last.Fm music platform.Source: SocInfo2013 - Social Informatics. 5th International Conference, pp. 319–332, Kyoto, Japan, 25-27 November 2013
DOI: 10.1007/978-3-319-03260-3_28
Project(s): DATA SIM
Metrics:
Pennacchioli D., Rossetti G., Pappalardo L., Pedreschi D., Giannotti F., Coscia M.
One classic problem denition in social network analysis is the study of diusion in networks, which enables us to tackle problems like favoring the adoption of positive technologies. Most of the attention has been turned to how to maximize the number of in uenced nodes, but this approach misses the fact that dierent scenarios imply dierent dif- fusion dynamics, only slightly related to maximizing the number of nodes involved. In this paper we measure three dierent dimensions of social prominence: the Width, i.e. the ratio of neighbors in uenced by a node; the Depth, i.e. the degrees of separation from a node to the nodes perceiv- ing its prominence; and the Strength, i.e. the intensity of the prominence of a node. By dening a procedure to extract prominent users in complex networks, we detect associations between the three dimensions of social prominence and classical network statistics. We validate our results on a social network extracted from the Last.Fm music platform.Source: SocInfo2013 - Social Informatics. 5th International Conference, pp. 319–332, Kyoto, Japan, 25-27 November 2013
DOI: 10.1007/978-3-319-03260-3_28
Project(s): DATA SIM
Metrics:
See at:
www.michelecoscia.com 
| doi.org | link.springer.com | CNR ExploRA
2013
Contribution to book
Unknown
Mobility and geo-social networks
Spinsanti L., Berlingerio M., Pappalardo L.
The Social Web is changing the way people create and use information. Every day millions of pieces of information are shared through the structure of many online social networks such as Facebook, Google+,Twitter, Foursquare, and so on. People have discovered a new way to exploit their sociality: from work to entertainment, from new participatory journalism to religion, from global to local government, from disaster management to market advertisement, from momently personal status update to milestone family events, the trend is to be social. Information or content are shared by users through the web by posting images or videos (e.g. on Flickr or YouTube), blogging or micro-blogging (Twitter),surveying and updating geographic information (OpenStreeMap), or playing geographic-based games (FourSquare). Considering the increase in mobile Internet access through smartphones and the number of (geo)social media platforms, we can expect the amount of information to continuously grow in the near future.This contribution discusses on the following questions: In which ways may location information relate to generated content on the web? How might this location be captured and represented? Where are possible sources for uncertainty (with respect to the location information)? Mobility and Geosocial networks: How the trajectories footprints in real word can be retrieved in the web, (and vice versa)?Source: Mobility Data - Modeling, Management, and Understanding, edited by Chiara Renso, Stefano Spaccapietra, Esteban Zimányi, pp. 315–333, 2013
Spinsanti L., Berlingerio M., Pappalardo L.
The Social Web is changing the way people create and use information. Every day millions of pieces of information are shared through the structure of many online social networks such as Facebook, Google+,Twitter, Foursquare, and so on. People have discovered a new way to exploit their sociality: from work to entertainment, from new participatory journalism to religion, from global to local government, from disaster management to market advertisement, from momently personal status update to milestone family events, the trend is to be social. Information or content are shared by users through the web by posting images or videos (e.g. on Flickr or YouTube), blogging or micro-blogging (Twitter),surveying and updating geographic information (OpenStreeMap), or playing geographic-based games (FourSquare). Considering the increase in mobile Internet access through smartphones and the number of (geo)social media platforms, we can expect the amount of information to continuously grow in the near future.This contribution discusses on the following questions: In which ways may location information relate to generated content on the web? How might this location be captured and represented? Where are possible sources for uncertainty (with respect to the location information)? Mobility and Geosocial networks: How the trajectories footprints in real word can be retrieved in the web, (and vice versa)?Source: Mobility Data - Modeling, Management, and Understanding, edited by Chiara Renso, Stefano Spaccapietra, Esteban Zimányi, pp. 315–333, 2013
See at: CNR ExploRA
2013
Conference article
Restricted
Comparing general mobility and mobility by car
Pappalardo L., Simini F., Rinzivillo S., Pedreschi D., Giannotti F.
In the last years, the emergence of big data led scientists from diverse disciplines toward the study of the laws underlying human mobility. Although these recent discoveries have shed light on very interesting and fascinating aspects about people movements, they are generally focused on global and general mobility patterns. For this reason, they do not necessarily capture phenomena related to specific types of mobility, such as mobility by car, by public transportations means, by foot and so on. In this work, we aim to compare general human mobility with mobility expressed by a specific conveyance, trying to address the following question: What are the differences between general mobility and mobility by car? To answer this question, we present the results of an analysis performed on a big mobile phone dataset and on a GPS dataset storing information about car travels in Italy.Source: 1st BRICS Countries Congress and 11th Brazilian Congress on Computational Intelligence, Recife, Brazil, 8-11 September 2013
Pappalardo L., Simini F., Rinzivillo S., Pedreschi D., Giannotti F.
In the last years, the emergence of big data led scientists from diverse disciplines toward the study of the laws underlying human mobility. Although these recent discoveries have shed light on very interesting and fascinating aspects about people movements, they are generally focused on global and general mobility patterns. For this reason, they do not necessarily capture phenomena related to specific types of mobility, such as mobility by car, by public transportations means, by foot and so on. In this work, we aim to compare general human mobility with mobility expressed by a specific conveyance, trying to address the following question: What are the differences between general mobility and mobility by car? To answer this question, we present the results of an analysis performed on a big mobile phone dataset and on a GPS dataset storing information about car travels in Italy.Source: 1st BRICS Countries Congress and 11th Brazilian Congress on Computational Intelligence, Recife, Brazil, 8-11 September 2013
See at:
brics-cci.org | CNR ExploRA
2013
Conference article
Unknown
Measuring tie strength in multidimensional networks
Rossetti G., Pappalardo L., Pedreschi D.
Online social networks have allowed us to build massive net- works of weak ties: acquaintances and nonintimate ties we use all the time to spread information and thoughts. Conversely, strong ties are people we really trust, persons most like us and whose social circles tightly overlap with our own. Unfortunately, social media do not incorporate tie strength in the creation and management of relationships, and treat all users the same: friend or stranger, with little or nothing in between. In the current work, we address the challenging issue of detecting on online social networks the strong and intimate ties from the huge mass of such mere social contacts. In order to do so, we propose a novel multidimensional definition of tie strength which exploits the existence of multiple online social links between two individuals. We test our definition on a multidimensional network constructed over users in Foursquare, Twitter and Facebook, analyzing the structural role of strong e weak links, and the correlations with the most common similarity measures.Source: SEBD 2013 - 21st Italian Symposium on Advanced Database Systems, pp. 223–230, Rocella Jonica, Italy, June 30 - July 03 2013
Project(s): DATA SIM
Rossetti G., Pappalardo L., Pedreschi D.
Online social networks have allowed us to build massive net- works of weak ties: acquaintances and nonintimate ties we use all the time to spread information and thoughts. Conversely, strong ties are people we really trust, persons most like us and whose social circles tightly overlap with our own. Unfortunately, social media do not incorporate tie strength in the creation and management of relationships, and treat all users the same: friend or stranger, with little or nothing in between. In the current work, we address the challenging issue of detecting on online social networks the strong and intimate ties from the huge mass of such mere social contacts. In order to do so, we propose a novel multidimensional definition of tie strength which exploits the existence of multiple online social links between two individuals. We test our definition on a multidimensional network constructed over users in Foursquare, Twitter and Facebook, analyzing the structural role of strong e weak links, and the correlations with the most common similarity measures.Source: SEBD 2013 - 21st Italian Symposium on Advanced Database Systems, pp. 223–230, Rocella Jonica, Italy, June 30 - July 03 2013
Project(s): DATA SIM
See at: CNR ExploRA
2013
Conference article
Restricted
"Engine matters": a first large scale data driven study on cyclists' performance
Cintia P., Pappalardo L., Pedreschi D.
The recent emergence of the so called online social fitness constitutes a good proxy to study the patterns underlying success in sport. Through these platforms, users can collect, monitor and share with friends their sport performance, diet, and even burned calories, giving an unprecedented opportunity to answer very fascinating questions: What are the main factors that shape sport performance? What are the characteristics that distinguish successful sportsmen? Can we characterize the role of social influence on fitness behavior? In the current work, we present the results of a study conducted on a sample of 29, 284 cyclists downloaded via APIs from the social fitness platform Strava.com. We defined two basic metrics: a measure of training effort, that is how much a cyclist struggled during the workout; and a measure of training performance indicating the results achieved during the training. Analyzing the relationship between these two metrics, an interesting result immediately emerges: at a global level, there is no correlation between effort and performance. This means that, in general, the performance is not simply a function of training: two athletes with the same level of training have different performance. However, by deeply investigating workouts time evolution and cyclists' training characteristics, we found that athletes that better improve their performance follow precise training patterns usually referred as overcompensation theory, with alternation of stress peaks and rest periods. Studies and experiments related to such theory, up to now, have always been conducted by sports doctors on a few dozen professionals athletes. To the best of our knowledge, our study is the first corroboration on large scale of this theory, mainly confirming that "engine matters", but tuning is fundamental.Source: Workshop on Data Mining Case Studies and Practice Prize, pp. 147–153, Dallas, US, 7 December 2013
DOI: 10.1109/icdmw.2013.41
Metrics:
Cintia P., Pappalardo L., Pedreschi D.
The recent emergence of the so called online social fitness constitutes a good proxy to study the patterns underlying success in sport. Through these platforms, users can collect, monitor and share with friends their sport performance, diet, and even burned calories, giving an unprecedented opportunity to answer very fascinating questions: What are the main factors that shape sport performance? What are the characteristics that distinguish successful sportsmen? Can we characterize the role of social influence on fitness behavior? In the current work, we present the results of a study conducted on a sample of 29, 284 cyclists downloaded via APIs from the social fitness platform Strava.com. We defined two basic metrics: a measure of training effort, that is how much a cyclist struggled during the workout; and a measure of training performance indicating the results achieved during the training. Analyzing the relationship between these two metrics, an interesting result immediately emerges: at a global level, there is no correlation between effort and performance. This means that, in general, the performance is not simply a function of training: two athletes with the same level of training have different performance. However, by deeply investigating workouts time evolution and cyclists' training characteristics, we found that athletes that better improve their performance follow precise training patterns usually referred as overcompensation theory, with alternation of stress peaks and rest periods. Studies and experiments related to such theory, up to now, have always been conducted by sports doctors on a few dozen professionals athletes. To the best of our knowledge, our study is the first corroboration on large scale of this theory, mainly confirming that "engine matters", but tuning is fundamental.Source: Workshop on Data Mining Case Studies and Practice Prize, pp. 147–153, Dallas, US, 7 December 2013
DOI: 10.1109/icdmw.2013.41
Metrics:
See at:
doi.org | www.dataminingcasestudies.com | CNR ExploRA