2017
Contribution to book
Restricted
Assessing privacy risk in retail data
Pellungrini R, Pratesi F, Pappalardo L
Retail data are one of the most requested commodities by commercial companies. Unfortunately, from this data it is possible to retrieve highly sensitive information about individuals. Thus, there exists the need for accurate individual privacy risk evaluation. In this paper, we propose a methodology for assessing privacy risk in retail data. We define the data formats for representing retail data, the privacy framework for calculating privacy risk and some possible privacy attacks for this kind of data. We perform experiments in a real-world retail dataset, and show the distribution of privacy risk for the various attacks.DOI: 10.1007/978-3-319-71970-2_3
Project(s): SoBigData
Metrics:
Pellungrini R, Pratesi F, Pappalardo L
Retail data are one of the most requested commodities by commercial companies. Unfortunately, from this data it is possible to retrieve highly sensitive information about individuals. Thus, there exists the need for accurate individual privacy risk evaluation. In this paper, we propose a methodology for assessing privacy risk in retail data. We define the data formats for representing retail data, the privacy framework for calculating privacy risk and some possible privacy attacks for this kind of data. We perform experiments in a real-world retail dataset, and show the distribution of privacy risk for the various attacks.DOI: 10.1007/978-3-319-71970-2_3
Project(s): SoBigData
Metrics:
See at:
Lecture Notes in Computer Science 
| CNR IRIS | CNR IRIS | link.springer.com
2017
Other
Open Access
Predicting and explaining the popularity of songs with data mining
Campagna M., Pappalardo L.
Data mining techniques recently were used to solve several problems related to music. This dissertation studies songs popularity in order to find out factors that make a song popular or not. The outcomes obtained are also used to give an answer to the myth of four chords. This myth in fact asserts that all popular songs can be played by using only four chords. The entire project covers all the stages of Knowledge Discovery in the Databasesprocess. We aimed to make a first research on songs popularity. In particular, data on music songs are collected and studied. These data are also used to create several models using data mining techniques. The problem of predicting and explaining songs popularity is studied by using both regression and classification algorithms. Finally, the fittest model is interpreted and tested with specific instances in order to achieve the goal.Project(s): SoBigData
Campagna M., Pappalardo L.
Data mining techniques recently were used to solve several problems related to music. This dissertation studies songs popularity in order to find out factors that make a song popular or not. The outcomes obtained are also used to give an answer to the myth of four chords. This myth in fact asserts that all popular songs can be played by using only four chords. The entire project covers all the stages of Knowledge Discovery in the Databasesprocess. We aimed to make a first research on songs popularity. In particular, data on music songs are collected and studied. These data are also used to create several models using data mining techniques. The problem of predicting and explaining songs popularity is studied by using both regression and classification algorithms. Finally, the fittest model is interpreted and tested with specific instances in order to achieve the goal.Project(s): SoBigData
See at:
etd.adm.unipi.it 
| CNR IRIS | ISTI Repository | CNR IRIS
2017
Journal article
Open Access
Tiles: an online algorithm for community discovery in dynamic social networks
Rossetti G, Pappalardo L, Pedreschi D, Giannotti F
Community discovery has emerged during the last decade as one of the most challenging problems in social network analysis. Many algorithms have been proposed to find communities on static networks, i.e. networks which do not change in time. However, social networks are dynamic realities (e.g. call graphs, online social networks): in such scenarios static community discovery fails to identify a partition of the graph that is semantically consistent with the temporal information expressed by the data. In this work we propose Tiles, an algorithm that extracts overlapping communities and tracks their evolution in time following an online iterative procedure. Our algorithm operates following a domino effect strategy, dynamically recomputing nodes community memberships whenever a new interaction takes place. We compare Tiles with state-of-the-art community detection algorithms on both synthetic and real world networks having annotated community structure: our experiments show that the proposed approach is able to guarantee lower execution times and better correspondence with the ground truth communities than its competitors. Moreover, we illustrate the specifics of the proposed approach by discussing the properties of identified communities it is able to identify.Source: MACHINE LEARNING, vol. 106 (issue 8), pp. 1213-1241
DOI: 10.1007/s10994-016-5582-8
Project(s): CIMPLEX, SoBigData
Metrics:
Rossetti G, Pappalardo L, Pedreschi D, Giannotti F
Community discovery has emerged during the last decade as one of the most challenging problems in social network analysis. Many algorithms have been proposed to find communities on static networks, i.e. networks which do not change in time. However, social networks are dynamic realities (e.g. call graphs, online social networks): in such scenarios static community discovery fails to identify a partition of the graph that is semantically consistent with the temporal information expressed by the data. In this work we propose Tiles, an algorithm that extracts overlapping communities and tracks their evolution in time following an online iterative procedure. Our algorithm operates following a domino effect strategy, dynamically recomputing nodes community memberships whenever a new interaction takes place. We compare Tiles with state-of-the-art community detection algorithms on both synthetic and real world networks having annotated community structure: our experiments show that the proposed approach is able to guarantee lower execution times and better correspondence with the ground truth communities than its competitors. Moreover, we illustrate the specifics of the proposed approach by discussing the properties of identified communities it is able to identify.Source: MACHINE LEARNING, vol. 106 (issue 8), pp. 1213-1241
DOI: 10.1007/s10994-016-5582-8
Project(s): CIMPLEX
, SoBigData Metrics:
See at:
Machine Learning | CNR IRIS | link.springer.com | Machine Learning | ISTI Repository | 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
A data mining approach to assess privacy risk in human mobility data
Pellungrini R, Pappalardo L, Pratesi F, Monreale A
Human mobility data are an important proxy to understand human mobility dynamics, develop analytical services, and design mathematical models for simulation and what-if analysis. Unfortunately mobility data are very sensitive since they may enable the re-identification of individuals in a database. Existing frameworks for privacy risk assessment provide data providers with tools to control and mitigate privacy risks, but they suffer two main shortcomings: (i) they have a high computational complexity; (ii) the privacy risk must be recomputed every time new data records become available and for every selection of individuals, geographic areas, or time windows. In this article, we propose a fast and flexible approach to estimate privacy risk in human mobility data. The idea is to train classifiers to capture the relation between individual mobility patterns and the level of privacy risk of individuals. We show the effectiveness of our approach by an extensive experiment on real-world GPS data in two urban areas and investigate the relations between human mobility patterns and the privacy risk of individuals.Source: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY (PRINT), vol. 9 (issue 3), pp. 31:1-31:27
DOI: 10.1145/3106774
Project(s): SoBigData
Metrics:
Pellungrini R, Pappalardo L, Pratesi F, Monreale A
Human mobility data are an important proxy to understand human mobility dynamics, develop analytical services, and design mathematical models for simulation and what-if analysis. Unfortunately mobility data are very sensitive since they may enable the re-identification of individuals in a database. Existing frameworks for privacy risk assessment provide data providers with tools to control and mitigate privacy risks, but they suffer two main shortcomings: (i) they have a high computational complexity; (ii) the privacy risk must be recomputed every time new data records become available and for every selection of individuals, geographic areas, or time windows. In this article, we propose a fast and flexible approach to estimate privacy risk in human mobility data. The idea is to train classifiers to capture the relation between individual mobility patterns and the level of privacy risk of individuals. We show the effectiveness of our approach by an extensive experiment on real-world GPS data in two urban areas and investigate the relations between human mobility patterns and the privacy risk of individuals.Source: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY (PRINT), vol. 9 (issue 3), pp. 31:1-31:27
DOI: 10.1145/3106774
Project(s): SoBigData
Metrics:
See at:
ACM Transactions on Intelligent Systems and Technology | doi.acm.org | Archivio della Ricerca - Università di Pisa | CNR IRIS | ISTI Repository | ACM Transactions on Intelligent Systems and Technology | CNR IRIS
2017
Conference article
Open Access
Who is going to get hurt? Predicting injuries in professional soccer
Rossi A, Pappalardo L, Cintia P, Fernandez J, Iaia Fm, Medina D
Injury prevention has a fundamental role in professional soccer due to the high cost of recovery for players and the strong influence of injuries on a club's performance. In this paper we provide a predictive model to prevent injuries of soccer players using a multidimensional approach based on GPS measurements and machine learning. In an evolutive scenario, where a soccer club starts collecting the data for the first time and updates the predictive model as the season goes by, our approach can detect around half of the injuries, allowing the soccer club to save 70% of a season's economic costs related to injuries. The proposed approach can be a valuable support for coaches, helping the soccer club to reduce injury incidence, save money and increase team performance.Source: CEUR WORKSHOP PROCEEDINGS, pp. 21-30. Skopje, Macedonia, 18 September 2017
Project(s): SoBigData
Rossi A, Pappalardo L, Cintia P, Fernandez J, Iaia Fm, Medina D
Injury prevention has a fundamental role in professional soccer due to the high cost of recovery for players and the strong influence of injuries on a club's performance. In this paper we provide a predictive model to prevent injuries of soccer players using a multidimensional approach based on GPS measurements and machine learning. In an evolutive scenario, where a soccer club starts collecting the data for the first time and updates the predictive model as the season goes by, our approach can detect around half of the injuries, allowing the soccer club to save 70% of a season's economic costs related to injuries. The proposed approach can be a valuable support for coaches, helping the soccer club to reduce injury incidence, save money and increase team performance.Source: CEUR WORKSHOP PROCEEDINGS, pp. 21-30. Skopje, Macedonia, 18 September 2017
Project(s): SoBigData
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2017
Conference article
Restricted
Fast estimation of privacy risk in human mobility data
Pellungrini R, Pappalardo L, Pratesi F, Monreale A
Mobility data are an important proxy to understand the patterns of human movements, develop analytical services and design models for simulation and prediction of human dynamics. Unfortunately mobility data are also very sensitive, since they may contain personal information about the individuals involved. Existing frameworks for privacy risk assessment enable the data providers to quantify and mitigate privacy risks, but they suffer two main limitations: (i) they have a high computational complexity; (ii) the privacy risk must be re-computed for each new set of individuals, geographic areas or time windows. In this paper we explore a fast and flexible solution to estimate privacy risk in human mobility data, using predictive models to capture the relation between an individual's mobility patterns and her privacy risk. We show the effectiveness of our approach by experimentation on a real-world GPS dataset and provide a comparison with traditional methods.DOI: 10.1007/978-3-319-66284-8_35
Project(s): SoBigData
Metrics:
Pellungrini R, Pappalardo L, Pratesi F, Monreale A
Mobility data are an important proxy to understand the patterns of human movements, develop analytical services and design models for simulation and prediction of human dynamics. Unfortunately mobility data are also very sensitive, since they may contain personal information about the individuals involved. Existing frameworks for privacy risk assessment enable the data providers to quantify and mitigate privacy risks, but they suffer two main limitations: (i) they have a high computational complexity; (ii) the privacy risk must be re-computed for each new set of individuals, geographic areas or time windows. In this paper we explore a fast and flexible solution to estimate privacy risk in human mobility data, using predictive models to capture the relation between an individual's mobility patterns and her privacy risk. We show the effectiveness of our approach by experimentation on a real-world GPS dataset and provide a comparison with traditional methods.DOI: 10.1007/978-3-319-66284-8_35
Project(s): SoBigData
Metrics:
See at:
Lecture Notes in Computer Science | CNR IRIS | CNR IRIS | link.springer.com
2017
Other
Open Access
Transfer Network Analysis: evoluzione del calciomercato dal dopoguerra ai giorni nostri
Mastini S., Pappalardo L.
La Tesi si pone come obiettivo lo studio delle reti e delle basi di dati collegate al mondo dello sport, in particolare quello del calcio. L'analisi dei flussi di mercato del calcio, condotta seguendo la dottrina della data analytics, permette di capire come si sia evoluto il calciomercato nell'arco temporale che va dal 1950 al 2014. A partire dallo studio delle reti, portando a termine misure statistiche con granularità annua, è possibile estrapolare dati sull'internazionalizzazione del sistema mondiale e i momenti che ne hanno sancito i cambiamenti epocali. Questo tipo di approfondimento ci permette di monitorare anche lo stato dell'arte dei cinque maggiori campionati d'Europa (Serie A, Premier League, Liga, Ligue 1 e Bundesliga), ricostruendone così anche l'interconnessione tra i club che ne fanno parte. Dopo aver vagliato la situazione complessiva, ci si è poi focalizzati sulla ricostruzione delle linee temporali del calciomercato europeo, analizzando in primo luogo il tipo di trasferimento registrato e la sua incidenza nel tempo nonché la sua valenza sociale. Successivamente, cercando di scoprire gli escamotage e i bilanci finanziari che permettono ai club dei maggiori campionati europei di giustificare il proprio bilancio a fronte anche dei risultati sportivi ottenuti. L'indagine conseguita in tal senso, ha preso come punto di riferimento soprattutto la Serie A e la Premier League per ragioni di prestigio storico e non solo, anche per l'importanza avuta nel sancire il corso dell'evoluzione del calcio mondiale.Project(s): SoBigData
Mastini S., Pappalardo L.
La Tesi si pone come obiettivo lo studio delle reti e delle basi di dati collegate al mondo dello sport, in particolare quello del calcio. L'analisi dei flussi di mercato del calcio, condotta seguendo la dottrina della data analytics, permette di capire come si sia evoluto il calciomercato nell'arco temporale che va dal 1950 al 2014. A partire dallo studio delle reti, portando a termine misure statistiche con granularità annua, è possibile estrapolare dati sull'internazionalizzazione del sistema mondiale e i momenti che ne hanno sancito i cambiamenti epocali. Questo tipo di approfondimento ci permette di monitorare anche lo stato dell'arte dei cinque maggiori campionati d'Europa (Serie A, Premier League, Liga, Ligue 1 e Bundesliga), ricostruendone così anche l'interconnessione tra i club che ne fanno parte. Dopo aver vagliato la situazione complessiva, ci si è poi focalizzati sulla ricostruzione delle linee temporali del calciomercato europeo, analizzando in primo luogo il tipo di trasferimento registrato e la sua incidenza nel tempo nonché la sua valenza sociale. Successivamente, cercando di scoprire gli escamotage e i bilanci finanziari che permettono ai club dei maggiori campionati europei di giustificare il proprio bilancio a fronte anche dei risultati sportivi ottenuti. L'indagine conseguita in tal senso, ha preso come punto di riferimento soprattutto la Serie A e la Premier League per ragioni di prestigio storico e non solo, anche per l'importanza avuta nel sancire il corso dell'evoluzione del calcio mondiale.Project(s): SoBigData
See at:
etd.adm.unipi.it | CNR IRIS | ISTI Repository | CNR IRIS