2017
Other
Open Access
ISTI Young Research Award 2017
Barsocchi P., Basile D., Candela L., Ciancia V., Delle Piane M., Esuli A., Ferrari A., Girardi M., Guidotti R., Lonetti F., Moroni D., Nardini F. M., Rinzivillo S., Vadicamo L.
The ISTI Young Researcher Award is an award for young people of Institute of Information Science and Technologies (ISTI) with high scientific production. In particular, the award is granted to young staff members (less than 35 years old) by assessing the yearly scientific production of the year preceding the award. This report documents procedure and results of the 2017 edition of the award.Source: ISTI Technical reports, 2017
Barsocchi P., Basile D., Candela L., Ciancia V., Delle Piane M., Esuli A., Ferrari A., Girardi M., Guidotti R., Lonetti F., Moroni D., Nardini F. M., Rinzivillo S., Vadicamo L.
The ISTI Young Researcher Award is an award for young people of Institute of Information Science and Technologies (ISTI) with high scientific production. In particular, the award is granted to young staff members (less than 35 years old) by assessing the yearly scientific production of the year preceding the award. This report documents procedure and results of the 2017 edition of the award.Source: ISTI Technical reports, 2017
See at:
ISTI Repository 
| CNR ExploRA
2017
Contribution to book
Open Access
Personal Analytics and Privacy. An Individual and Collective Perspective
Guidotti R, Monreale A, Pedreschi D, Abiteboul S
The First International Workshop on Personal Analytics and Privacy (PAP) was held in Skopje, Macedonia, on September 18, 2017. The purpose of the workshop is to encourage principled research that will lead to the advancement of personal data analytics, personal services development, privacy, data protection, and privacy risk assessment with the intent of bringing together researchers and practitioners interested in personal analytics and privacy. The workshop, collocated with the conference ECML/PKDD 2017, sought top-quality submissions addressing important issues related to personal analytics, personal data mining, and privacy in the context where real individual data (spatio temporal data, call details records, tweets, mobility data, transactional data, social networking data, etc.) are used for developing data-driven services, for realizing social studies aimed at understanding nowadays society, and for publication purposes.DOI: 10.1007/978-3-319-71970-2
Project(s): SoBigData
Metrics:
Guidotti R, Monreale A, Pedreschi D, Abiteboul S
The First International Workshop on Personal Analytics and Privacy (PAP) was held in Skopje, Macedonia, on September 18, 2017. The purpose of the workshop is to encourage principled research that will lead to the advancement of personal data analytics, personal services development, privacy, data protection, and privacy risk assessment with the intent of bringing together researchers and practitioners interested in personal analytics and privacy. The workshop, collocated with the conference ECML/PKDD 2017, sought top-quality submissions addressing important issues related to personal analytics, personal data mining, and privacy in the context where real individual data (spatio temporal data, call details records, tweets, mobility data, transactional data, social networking data, etc.) are used for developing data-driven services, for realizing social studies aimed at understanding nowadays society, and for publication purposes.DOI: 10.1007/978-3-319-71970-2
Project(s): SoBigData
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | doi.org 
| CNR IRIS | CNR IRIS
2017
Contribution to book
Open Access
Personal Analytics and Privacy. An Individual and Collective Perspective: First International Workshop, PAP 2017, Held in Conjunction with ECML PKDD 2017, Skopje, Macedonia, September 18, 2017, Revised Selected Papers
Guidotti R, Monreale A, Pedreschi D, Abiteboul S
This book constitutes the thoroughly refereed post-conference proceedings of the First International Workshop on Personal Analytics and Privacy, PAP 2017, held in Skopje, Macedonia, in September 2017. The 14 papers presented together with 2 invited talks in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as personal analytics, personal data mining and privacy in the context where real individual data are used for developing a data-driven service, for realizing a social study aimed at understanding nowadays society, and for publication purposes.DOI: 10.1007/978-3-319-71970-2
Project(s): SoBigData
Metrics:
Guidotti R, Monreale A, Pedreschi D, Abiteboul S
This book constitutes the thoroughly refereed post-conference proceedings of the First International Workshop on Personal Analytics and Privacy, PAP 2017, held in Skopje, Macedonia, in September 2017. The 14 papers presented together with 2 invited talks in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as personal analytics, personal data mining and privacy in the context where real individual data are used for developing a data-driven service, for realizing a social study aimed at understanding nowadays society, and for publication purposes.DOI: 10.1007/978-3-319-71970-2
Project(s): SoBigData
Metrics:
See at:
CNR IRIS | ISTI Repository | www.springer.com | doi.org | CNR IRIS | CNR IRIS
2017
Journal article
Open Access
MyWay: location prediction via mobility profiling
Trasarti R, Guidotti R, Monreale A, Giannotti F
Forecasting the future positions of mobile users is a valuable task allowing us to operate efficiently a myriad of different applications which need this type of information. We propose MyWay, a prediction system which exploits the individual systematic behaviors modeled by mobility profiles to predict human movements. MyWay provides three strategies: the individual strategy uses only the user individual mobility profile, the collective strategy takes advantage of all users individual systematic behaviors, and the hybrid strategy that is a combination of the previous two. A key point is that MyWay only requires the sharing of individual mobility profiles, a concise representation of the user's movements, instead of raw trajectory data revealing the detailed movement of the users. We evaluate the prediction performances of our proposal by a deep experimentation on large real-world data. The results highlight that the synergy between the individual and collective knowledge is the key for a better prediction and allow the system to outperform the state-of-art methods.Source: INFORMATION SYSTEMS, vol. 64, pp. 350-367
DOI: 10.1016/j.is.2015.11.002
Project(s): SoBigData
Metrics:
Trasarti R, Guidotti R, Monreale A, Giannotti F
Forecasting the future positions of mobile users is a valuable task allowing us to operate efficiently a myriad of different applications which need this type of information. We propose MyWay, a prediction system which exploits the individual systematic behaviors modeled by mobility profiles to predict human movements. MyWay provides three strategies: the individual strategy uses only the user individual mobility profile, the collective strategy takes advantage of all users individual systematic behaviors, and the hybrid strategy that is a combination of the previous two. A key point is that MyWay only requires the sharing of individual mobility profiles, a concise representation of the user's movements, instead of raw trajectory data revealing the detailed movement of the users. We evaluate the prediction performances of our proposal by a deep experimentation on large real-world data. The results highlight that the synergy between the individual and collective knowledge is the key for a better prediction and allow the system to outperform the state-of-art methods.Source: INFORMATION SYSTEMS, vol. 64, pp. 350-367
DOI: 10.1016/j.is.2015.11.002
Project(s): SoBigData
Metrics:
See at:
Information Systems | CNR IRIS | ISTI Repository | www.sciencedirect.com | Information Systems | CNR IRIS | CNR IRIS
2017
Conference article
Open Access
Clustering individual transactional data for masses of users
Guidotti R, Monreale A, Nanni M, Giannotti F, Pedreschi D
Mining a large number of datasets recording human activities for making sense of individual data is the key enabler of a new wave of personalized knowledge-based services. In this paper we focus on the problem of clustering individual transactional data for a large mass of users. Transactional data is a very pervasive kind of information that is collected by several services, often involving huge pools of users. We propose txmeans, a parameter-free clustering algorithm able to efficiently partitioning transactional data in a completely automatic way. Txmeans is designed for the case where clustering must be applied on a massive number of different datasets, for instance when a large set of users need to be analyzed individually and each of them has generated a long history of transactions. A deep experimentation on both real and synthetic datasets shows the practical effectiveness of txmeans for the mass clustering of different personal datasets, and suggests that txmeans outperforms existing methods in terms of quality and efficiency. Finally, we present a personal cart assistant application based on txmeans.DOI: 10.1145/3097983.3098034
Project(s): SoBigData
Metrics:
Guidotti R, Monreale A, Nanni M, Giannotti F, Pedreschi D
Mining a large number of datasets recording human activities for making sense of individual data is the key enabler of a new wave of personalized knowledge-based services. In this paper we focus on the problem of clustering individual transactional data for a large mass of users. Transactional data is a very pervasive kind of information that is collected by several services, often involving huge pools of users. We propose txmeans, a parameter-free clustering algorithm able to efficiently partitioning transactional data in a completely automatic way. Txmeans is designed for the case where clustering must be applied on a massive number of different datasets, for instance when a large set of users need to be analyzed individually and each of them has generated a long history of transactions. A deep experimentation on both real and synthetic datasets shows the practical effectiveness of txmeans for the mass clustering of different personal datasets, and suggests that txmeans outperforms existing methods in terms of quality and efficiency. Finally, we present a personal cart assistant application based on txmeans.DOI: 10.1145/3097983.3098034
Project(s): SoBigData
Metrics:
See at:
arpi.unipi.it | dl.acm.org | Archivio della Ricerca - Università di Pisa | CNR IRIS | ISTI Repository | doi.org | CNR IRIS | CNR IRIS
2017
Conference article
Open Access
There's a Path for Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas
Guidotti R, Trasarti R, Nanni M, Giannotti F, Pedreschi D
The avalanche of mobility data like GPS and GSM daily produced by each user through mobile devices enables personalized mobility-services improving everyday life. The base for these mobility-services lies in the predictability of human behavior. In this paper we propose an approach for reproducing the user's personal mobility agenda that is able to predict the user's positions for the whole day. We reproduce the agenda by exploiting a data-driven personal mobility model able to capture and summarize different aspects of the systematic mobility behavior of a user. We show how the proposed approach outperforms typical methodologies adopted in the literature on four different real GPS datasets. Moreover, we analyze some features of the mobility models and we discuss how they can be employed as agents of a simulator for what-if mobility analysis.DOI: 10.1109/dsaa.2017.12
Project(s): SoBigData
Metrics:
Guidotti R, Trasarti R, Nanni M, Giannotti F, Pedreschi D
The avalanche of mobility data like GPS and GSM daily produced by each user through mobile devices enables personalized mobility-services improving everyday life. The base for these mobility-services lies in the predictability of human behavior. In this paper we propose an approach for reproducing the user's personal mobility agenda that is able to predict the user's positions for the whole day. We reproduce the agenda by exploiting a data-driven personal mobility model able to capture and summarize different aspects of the systematic mobility behavior of a user. We show how the proposed approach outperforms typical methodologies adopted in the literature on four different real GPS datasets. Moreover, we analyze some features of the mobility models and we discuss how they can be employed as agents of a simulator for what-if mobility analysis.DOI: 10.1109/dsaa.2017.12
Project(s): SoBigData
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | doi.org | Archivio istituzionale della Ricerca - Scuola Normale Superiore | CNR IRIS
2017
Conference article
Open Access
Market basket prediction using user-centric temporal annotated recurring sequences
Guidotti R, Rossetti G, Pappalardo L, Giannotti F, Pedreschi D
Nowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer's decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern named Temporal Annotated Recurring Sequence (TARS). We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer's stocks and recommend the set of most necessary items. A deep experimentation shows that TARS can explain the customers' purchase behavior, and that TBP outperforms the state-of-the-art competitors.DOI: 10.1109/icdm.2017.111
DOI: 10.13140/rg.2.2.13033.19042
Project(s): SoBigData
Metrics:
Guidotti R, Rossetti G, Pappalardo L, Giannotti F, Pedreschi D
Nowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer's decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern named Temporal Annotated Recurring Sequence (TARS). We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer's stocks and recommend the set of most necessary items. A deep experimentation shows that TARS can explain the customers' purchase behavior, and that TBP outperforms the state-of-the-art competitors.DOI: 10.1109/icdm.2017.111
DOI: 10.13140/rg.2.2.13033.19042
Project(s): SoBigData
Metrics:
See at:
arxiv.org | CNR IRIS | ieeexplore.ieee.org | ISTI Repository | Archivio istituzionale della Ricerca - Scuola Normale Superiore | doi.org | ResearchGate Data | CNR IRIS | CNR IRIS
2017
Journal article
Open Access
The GRAAL of carpooling: GReen And sociAL optimization from crowd-sourced data
Berlingerio M, Ghaddar B, Guidotti R, Pascale A, Sassi A
Carpooling, i.e. the sharing of vehicles to reach common destinations, is often performed to reduce costs and pollution. Recent work on carpooling takes into account, besides mobility matches, also social aspects and, more generally, non-monetary incentives. In line with this, we present GRAAL, a data-driven methodology for GReen And sociAL carpooling. GRAAL optimizes a carpooling system not only by minimizing the number of cars needed at the city level, but also by maximizing the enjoyability of people sharing a trip. We introduce a measure of enjoyability based on people's interests, social links, and tendency to connect to people with similar or dissimilar interests. GRAAL computes the enjoyability within a set of users from crowd-sourced data, and then uses it on real world datasets to optimize a weighted linear combination of number of cars and enjoyability. To tune this weight, and to investigate the users' interest on the social aspects of carpooling, we conducted an online survey on potential carpooling users. We present the results of applying GRAAL on real world crowd-sourced data from the cities of Rome and San Francisco. Computational results are presented from both the city and the user perspective. Using the crowd-sourced weight, GRAAL is able to significantly reduce the number of cars needed, while keeping a high level of enjoyability on the tested data-set. From the user perspective, we show how the entire per-car distribution of enjoyability is increased with respect to the baselines.Source: TRANSPORTATION RESEARCH. PART C, EMERGING TECHNOLOGIES, vol. 80, pp. 20-36
DOI: 10.1016/j.trc.2017.02.025
Project(s): PETRA
Metrics:
Berlingerio M, Ghaddar B, Guidotti R, Pascale A, Sassi A
Carpooling, i.e. the sharing of vehicles to reach common destinations, is often performed to reduce costs and pollution. Recent work on carpooling takes into account, besides mobility matches, also social aspects and, more generally, non-monetary incentives. In line with this, we present GRAAL, a data-driven methodology for GReen And sociAL carpooling. GRAAL optimizes a carpooling system not only by minimizing the number of cars needed at the city level, but also by maximizing the enjoyability of people sharing a trip. We introduce a measure of enjoyability based on people's interests, social links, and tendency to connect to people with similar or dissimilar interests. GRAAL computes the enjoyability within a set of users from crowd-sourced data, and then uses it on real world datasets to optimize a weighted linear combination of number of cars and enjoyability. To tune this weight, and to investigate the users' interest on the social aspects of carpooling, we conducted an online survey on potential carpooling users. We present the results of applying GRAAL on real world crowd-sourced data from the cities of Rome and San Francisco. Computational results are presented from both the city and the user perspective. Using the crowd-sourced weight, GRAAL is able to significantly reduce the number of cars needed, while keeping a high level of enjoyability on the tested data-set. From the user perspective, we show how the entire per-car distribution of enjoyability is increased with respect to the baselines.Source: TRANSPORTATION RESEARCH. PART C, EMERGING TECHNOLOGIES, vol. 80, pp. 20-36
DOI: 10.1016/j.trc.2017.02.025
Project(s): PETRA
Metrics:
See at:
CNR IRIS | ISTI Repository | www.sciencedirect.com | Transportation Research Part C Emerging Technologies | CNR IRIS | CNR IRIS
2017
Other
Restricted
Personal Data Analytics: Capturing Human Behavior to Improve Self-Awareness and Personal Services through Individual and Collective Knowledge
Guidotti R
In the era of Big Data, every single user of our hyper-connected world leaves behind a myriad of digital breadcrumbs while performing her daily activities. It is sufficient to think of a simple smartphone that enables each one of us to browse the Web, listen to music on online musical services, post messages on social networks, perform online shopping sessions, acquire images and videos and record our geographical locations. This enormous amount of personal data could be exploited to improve the lifestyle of each individual by extracting, analyzing and exploiting user's behavioral patterns like the items frequently purchased, the routinary movements, the favorite sequence of songs listened, etc. However, even though some user-centric models for data management named Personal Data Store are emerging, currently there is still a significant lack in terms of algorithms and models specifically designed to extract and capture knowledge from personal data. This thesis proposes an extension to the idea of Personal Data Store through Personal Data Analytics. In practice, we describe parameter-free algorithms that do not need to be tuned by experts and are able to automatically extract the patterns from the user's data. We define personal data models to characterize the user profile which are able to capture and collect the users' behavioral patterns. In addition, we propose individual and collective services exploiting the knowledge extracted with Personal Data Analytics algorithm and models. The services are provided for the users which are organized in a Personal Data Ecosystem in form of a peer distributed network, and are available to share part of their own patterns as a return of the service providing. We show how the sharing with the collectivity enables or improves, the services analyzed. The sharing enhances the level of the service for individuals, for example by providing to the user an invaluable opportunity for having a better perception of her self-awareness. Moreover, at the same time, knowledge sharing can lead to forms of collective gain, like the reduction of the number of circulating cars. To prove the feasibility of Personal Data Analytics in terms of algorithms, models and services proposed we report an extensive experimentation on real world data.Project(s): CIMPLEX, PETRA , SoBigData
Guidotti R
In the era of Big Data, every single user of our hyper-connected world leaves behind a myriad of digital breadcrumbs while performing her daily activities. It is sufficient to think of a simple smartphone that enables each one of us to browse the Web, listen to music on online musical services, post messages on social networks, perform online shopping sessions, acquire images and videos and record our geographical locations. This enormous amount of personal data could be exploited to improve the lifestyle of each individual by extracting, analyzing and exploiting user's behavioral patterns like the items frequently purchased, the routinary movements, the favorite sequence of songs listened, etc. However, even though some user-centric models for data management named Personal Data Store are emerging, currently there is still a significant lack in terms of algorithms and models specifically designed to extract and capture knowledge from personal data. This thesis proposes an extension to the idea of Personal Data Store through Personal Data Analytics. In practice, we describe parameter-free algorithms that do not need to be tuned by experts and are able to automatically extract the patterns from the user's data. We define personal data models to characterize the user profile which are able to capture and collect the users' behavioral patterns. In addition, we propose individual and collective services exploiting the knowledge extracted with Personal Data Analytics algorithm and models. The services are provided for the users which are organized in a Personal Data Ecosystem in form of a peer distributed network, and are available to share part of their own patterns as a return of the service providing. We show how the sharing with the collectivity enables or improves, the services analyzed. The sharing enhances the level of the service for individuals, for example by providing to the user an invaluable opportunity for having a better perception of her self-awareness. Moreover, at the same time, knowledge sharing can lead to forms of collective gain, like the reduction of the number of circulating cars. To prove the feasibility of Personal Data Analytics in terms of algorithms, models and services proposed we report an extensive experimentation on real world data.Project(s): CIMPLEX
, PETRA , SoBigData