2011
Conference article
Unknown
From movement tracks through events to places: extracting and characterizing significant places from mobility data
Andrienko G., Andrienko N., Hurter C., Rinzivillo S., Wroebel S.
We propose a visual analytics procedure for analyzing movement data, i.e., recorded tracks of moving objects. It is oriented to a class of problems where it is required to determine significant places on the basis of certain types of events occurring repeatedly in movement data. The procedure consists of four major steps: (1) event extraction from trajectories; (2) event clustering and extraction of relevant places; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps are scalable with respect to the amount of the data under analysis. We demonstrate the use of the procedure by example of two real- world problems requiring analysis at different spatial scales.Source: IEEE Conference on Visual Analytics Science and Technology, VAST 2011, pp. 161–170, Providence, Rhode Island, October 23 -28 2011
Andrienko G., Andrienko N., Hurter C., Rinzivillo S., Wroebel S.
We propose a visual analytics procedure for analyzing movement data, i.e., recorded tracks of moving objects. It is oriented to a class of problems where it is required to determine significant places on the basis of certain types of events occurring repeatedly in movement data. The procedure consists of four major steps: (1) event extraction from trajectories; (2) event clustering and extraction of relevant places; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps are scalable with respect to the amount of the data under analysis. We demonstrate the use of the procedure by example of two real- world problems requiring analysis at different spatial scales.Source: IEEE Conference on Visual Analytics Science and Technology, VAST 2011, pp. 161–170, Providence, Rhode Island, October 23 -28 2011
See at: CNR ExploRA
2013
Journal article
Open Access
Scalable analysis of movement data for extracting and exploring significant places
Andrienko G., Andrienko N., Hurter C., Rinzivillo S., Wroebel S.
Place-oriented analysis of movement data, i.e., recorded tracks of moving objects, includes finding places of interest in which certain types of movement events occur repeatedly and investigating the temporal distribution of event occurrences in these places and, possibly, other characteristics of the places and links between them. For this class of problems, we propose a visual analytics procedure consisting of four major steps: (1) event extraction from trajectories; (2) extraction of relevant places based on event clustering; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps can be fulfilled in a scalable way with respect to the amount of the data under analysis; therefore, the procedure is not limited by the size of the computer's RAM and can be applied to very large datasets. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales.Source: IEEE transactions on visualization and computer graphics (Online) 19 (2013): 1078–1094. doi:10.1109/TVCG.2012.311
DOI: 10.1109/tvcg.2012.311
Metrics:
Andrienko G., Andrienko N., Hurter C., Rinzivillo S., Wroebel S.
Place-oriented analysis of movement data, i.e., recorded tracks of moving objects, includes finding places of interest in which certain types of movement events occur repeatedly and investigating the temporal distribution of event occurrences in these places and, possibly, other characteristics of the places and links between them. For this class of problems, we propose a visual analytics procedure consisting of four major steps: (1) event extraction from trajectories; (2) extraction of relevant places based on event clustering; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps can be fulfilled in a scalable way with respect to the amount of the data under analysis; therefore, the procedure is not limited by the size of the computer's RAM and can be applied to very large datasets. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales.Source: IEEE transactions on visualization and computer graphics (Online) 19 (2013): 1078–1094. doi:10.1109/TVCG.2012.311
DOI: 10.1109/tvcg.2012.311
Metrics:
See at:
IEEE Transactions on Visualization and Computer Graphics 
| City Research Online | IEEE Transactions on Visualization and Computer Graphics 
| Hyper Article en Ligne | ieeexplore.ieee.org | Fraunhofer-ePrints | CNR ExploRA
2013
Contribution to book
Restricted
Privacy-preserving Distributed Movement Data Aggregation
Monreale A., Wang W. H., Pratesi F., Rinzivillo S., Pedreschi D., Andrienko G., Andrienko N.
We propose a novel approach to privacy-preserving analytical processing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by individual vehicles and shipped to a central server. Movement data are sensitive because people's whereabouts have the potential to reveal intimate personal traits, such as religious or sexual preferences, and may allow re-identification of individuals in a database. We provide a privacy-preserving framework for movement data aggregation based on trajectory generalization in a distributed environment. The proposed solution, based on the differential privacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the effectiveness of our approach also in terms of data utility preserved by the data transformation.Source: Geographic Information Science at the Heart of Europe, edited by Danny Vandenbroucke, Bénédicte Bucher, Joep Crompvoets, pp. 225–245, 2013
DOI: 10.1007/978-3-319-00615-4_13
Project(s): DATA SIM
Metrics:
Monreale A., Wang W. H., Pratesi F., Rinzivillo S., Pedreschi D., Andrienko G., Andrienko N.
We propose a novel approach to privacy-preserving analytical processing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by individual vehicles and shipped to a central server. Movement data are sensitive because people's whereabouts have the potential to reveal intimate personal traits, such as religious or sexual preferences, and may allow re-identification of individuals in a database. We provide a privacy-preserving framework for movement data aggregation based on trajectory generalization in a distributed environment. The proposed solution, based on the differential privacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the effectiveness of our approach also in terms of data utility preserved by the data transformation.Source: Geographic Information Science at the Heart of Europe, edited by Danny Vandenbroucke, Bénédicte Bucher, Joep Crompvoets, pp. 225–245, 2013
DOI: 10.1007/978-3-319-00615-4_13
Project(s): DATA SIM
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2013
Report
Unknown
Differential privacy in distributed mobility analytics
Monreale A., Wang W. H., Pratesi F., Rinzivillo S., Pedreschi D., Andrienko G., Andrienko N.
Movement data are sensitive, because people's whereabouts may allow re- identification of individuals in a de-identified database and thus can potentially reveal intimate personal traits, such as religious or sexual preferences. In this paper, we focus on a distributed setting in which movement data from individual vehicles are collected and aggregated by a centralized station. We propose a novel approach to privacy-preserving analytical processing within such a distributed setting, and tackle the problem of obtaining aggregated traffic information while preventing privacy leakage from data collection and aggregation. We study and analyze three different solutions based on the differential privacy model and on sketching techniques for efficient data compression. Each solution achieves different trade-off between privacy protection and utility of the transformed data. Using real-life data, we demonstrate the effectiveness of our approaches in terms of data utility preserved by the data transformation, thus bringing empirical evidence to the fact that the "privacy-by-design" paradigm in big data analytics has the potential of delivering high data protection combined with high quality even in massively distributed techno-social systems.Source: ISTI Technical reports, 2013
Monreale A., Wang W. H., Pratesi F., Rinzivillo S., Pedreschi D., Andrienko G., Andrienko N.
Movement data are sensitive, because people's whereabouts may allow re- identification of individuals in a de-identified database and thus can potentially reveal intimate personal traits, such as religious or sexual preferences. In this paper, we focus on a distributed setting in which movement data from individual vehicles are collected and aggregated by a centralized station. We propose a novel approach to privacy-preserving analytical processing within such a distributed setting, and tackle the problem of obtaining aggregated traffic information while preventing privacy leakage from data collection and aggregation. We study and analyze three different solutions based on the differential privacy model and on sketching techniques for efficient data compression. Each solution achieves different trade-off between privacy protection and utility of the transformed data. Using real-life data, we demonstrate the effectiveness of our approaches in terms of data utility preserved by the data transformation, thus bringing empirical evidence to the fact that the "privacy-by-design" paradigm in big data analytics has the potential of delivering high data protection combined with high quality even in massively distributed techno-social systems.Source: ISTI Technical reports, 2013
See at: CNR ExploRA
2015
Journal article
Open Access
Exploiting spatial abstraction in predictive analytics of vehicle traffic
Andrienko N., Andrienko G., Rinzivillo S.
By applying visual analytics techniques to vehicle traffic data, we found a way to visualize and study the relationships between the traffic intensity and movement speed on links of a spatially abstracted transportation network. We observed that the traffic intensities and speeds in an abstracted network are interrelated in the same way as they are in a detailed street network at the level of street segments. We developed interactive visual interfaces that support representing these interdependencies by mathematical models. To test the possibility of utilizing them for performing traffic simulations on the basis of abstracted transportation networks, we devised a prototypical simulation algorithm employing these dependency models. The algorithm is embedded in an interactive visual environment for defining traffic scenarios, running simulations, and exploring their results. Our research demonstrates a principal possibility of performing traffic simulations on the basis of spatially abstracted transportation networks using dependency models derived from real traffic data. This possibility needs to be comprehensively investigated and tested in collaboration with transportation domain specialists.Source: ISPRS international journal of geo-information 4 (2015): 591–606. doi:10.3390/ijgi4020591
DOI: 10.3390/ijgi4020591
Project(s): SoBigData
Metrics:
Andrienko N., Andrienko G., Rinzivillo S.
By applying visual analytics techniques to vehicle traffic data, we found a way to visualize and study the relationships between the traffic intensity and movement speed on links of a spatially abstracted transportation network. We observed that the traffic intensities and speeds in an abstracted network are interrelated in the same way as they are in a detailed street network at the level of street segments. We developed interactive visual interfaces that support representing these interdependencies by mathematical models. To test the possibility of utilizing them for performing traffic simulations on the basis of abstracted transportation networks, we devised a prototypical simulation algorithm employing these dependency models. The algorithm is embedded in an interactive visual environment for defining traffic scenarios, running simulations, and exploring their results. Our research demonstrates a principal possibility of performing traffic simulations on the basis of spatially abstracted transportation networks using dependency models derived from real traffic data. This possibility needs to be comprehensively investigated and tested in collaboration with transportation domain specialists.Source: ISPRS international journal of geo-information 4 (2015): 591–606. doi:10.3390/ijgi4020591
DOI: 10.3390/ijgi4020591
Project(s): SoBigData
Metrics:
See at:
ISPRS International Journal of Geo-Information | City Research Online | ISTI Repository | ISPRS International Journal of Geo-Information | ISPRS International Journal of Geo-Information | Fraunhofer-ePrints | CNR ExploRA
2016
Journal article
Open Access
Leveraging spatial abstraction in traffic analysis and forecasting with visual analytics
Andrienko N., Andrienko G., Rinzivillo S.
A spatially abstracted transportation network is a graph where nodes are territory compartments (areas in geographic space) and edges, or links, are abstract constructs, each link representing all possible paths between two neighboring areas. By applying visual analytics techniques to vehicle traffic data from different territories, we discovered that the traffic intensity (a.k.a. traffic flow or traffic flux) and the mean velocity are interrelated in a spatially abstracted transportation network in the same way as at the level of street segments. Moreover, these relationships are consistent across different levels of spatial abstraction of a physical transportation network. Graphical representations of the flux-velocity interdependencies for abstracted links have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction. We propose a framework in which visual analytics supports three high-level tasks, assess, forecast, and develop options, in application to vehicle traffic. These tasks can be carried out in a coherent workflow, where each next task uses the results of the previous one(s). At the 'assess' stage, vehicle trajectories are used to build a spatially abstracted transportation network and compute the traffic intensities and mean velocities on the abstracted links by time intervals. The interdependencies between the two characteristics of the links are extracted and represented by formal models, which enable the second step of the workflow, 'forecast', involving simulation of vehicle movements under various conditions. The previously derived models allow not only prediction of normal traffic flows conforming to the regular daily and weekly patterns but also simulation of traffic in extraordinary cases, such as road closures, major public events, or mass evacuation due to a disaster. Interactive visual tools support preparation of simulations and analysis of their results. When the simulation forecasts problematic situations, such as major congestions and delays, the analyst proceeds to the step 'develop options' for trying various actions aimed at situation improvement and investigating their consequences. Action execution can be imitated by interactively modifying the input of the simulation model. Specific techniques support comparisons between results of simulating different "what if" scenarios.Source: Information systems (Oxf.) 57 (2016): 172–194. doi:10.1016/j.is.2015.08.007
DOI: 10.1016/j.is.2015.08.007
Project(s): CIMPLEX, SoBigData
Metrics:
Andrienko N., Andrienko G., Rinzivillo S.
A spatially abstracted transportation network is a graph where nodes are territory compartments (areas in geographic space) and edges, or links, are abstract constructs, each link representing all possible paths between two neighboring areas. By applying visual analytics techniques to vehicle traffic data from different territories, we discovered that the traffic intensity (a.k.a. traffic flow or traffic flux) and the mean velocity are interrelated in a spatially abstracted transportation network in the same way as at the level of street segments. Moreover, these relationships are consistent across different levels of spatial abstraction of a physical transportation network. Graphical representations of the flux-velocity interdependencies for abstracted links have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction. We propose a framework in which visual analytics supports three high-level tasks, assess, forecast, and develop options, in application to vehicle traffic. These tasks can be carried out in a coherent workflow, where each next task uses the results of the previous one(s). At the 'assess' stage, vehicle trajectories are used to build a spatially abstracted transportation network and compute the traffic intensities and mean velocities on the abstracted links by time intervals. The interdependencies between the two characteristics of the links are extracted and represented by formal models, which enable the second step of the workflow, 'forecast', involving simulation of vehicle movements under various conditions. The previously derived models allow not only prediction of normal traffic flows conforming to the regular daily and weekly patterns but also simulation of traffic in extraordinary cases, such as road closures, major public events, or mass evacuation due to a disaster. Interactive visual tools support preparation of simulations and analysis of their results. When the simulation forecasts problematic situations, such as major congestions and delays, the analyst proceeds to the step 'develop options' for trying various actions aimed at situation improvement and investigating their consequences. Action execution can be imitated by interactively modifying the input of the simulation model. Specific techniques support comparisons between results of simulating different "what if" scenarios.Source: Information systems (Oxf.) 57 (2016): 172–194. doi:10.1016/j.is.2015.08.007
DOI: 10.1016/j.is.2015.08.007
Project(s): CIMPLEX
, SoBigData Metrics:
See at:
Information Systems | City Research Online | ISTI Repository | Information Systems | Fraunhofer-ePrints | www.sciencedirect.com | CNR ExploRA
2015
Conference article
Restricted
Detection, tracking, and visualization of spatial event clusters for real time monitoring
Andrienko N., Andrienko G., Fuchs G., Rinzivillo S., Betz H.
Spatial events, such as lightning strikes or drops in moving vehicle speed, can be conceptualized as points in the space-time continuum. We consider real time monitoring scenarios in which the observer needs to detect significant (i.e., sufficiently big) spatio-temporal clusters of events as soon as they occur and track the further evolution of these clusters. Isolated spatial events and small clusters are of no interest (i.e., treated as noise) and should be hidden from the observer to avoid attention distraction and perceptual overload. The existing methods for stream clustering cannot enable on-the-fly separation of event clusters from the noise and immediate presentation of significant clusters and their evolution. We propose a novel algorithm tailored to this specific task and a visual analytics system that supports event stream monitoring by presenting detected event clusters and their evolution to the observer in real time.Source: IEEE International Conference on Data Science and Advanced Analytics, Paris, France, 19-21/10/2015
DOI: 10.1109/dsaa.2015.7344880
Project(s): SoBigData
Metrics:
Andrienko N., Andrienko G., Fuchs G., Rinzivillo S., Betz H.
Spatial events, such as lightning strikes or drops in moving vehicle speed, can be conceptualized as points in the space-time continuum. We consider real time monitoring scenarios in which the observer needs to detect significant (i.e., sufficiently big) spatio-temporal clusters of events as soon as they occur and track the further evolution of these clusters. Isolated spatial events and small clusters are of no interest (i.e., treated as noise) and should be hidden from the observer to avoid attention distraction and perceptual overload. The existing methods for stream clustering cannot enable on-the-fly separation of event clusters from the noise and immediate presentation of significant clusters and their evolution. We propose a novel algorithm tailored to this specific task and a visual analytics system that supports event stream monitoring by presenting detected event clusters and their evolution to the observer in real time.Source: IEEE International Conference on Data Science and Advanced Analytics, Paris, France, 19-21/10/2015
DOI: 10.1109/dsaa.2015.7344880
Project(s): SoBigData
Metrics:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA
2015
Conference article
Open Access
Real Time Detection and Tracking of Spatial Event Clusters
Andrienko N., Andrienko G., Fuchs G., Rinzivillo S., Betz H. D.
We demonstrate a system of tools for real-time detection of significant clusters of spatial events and observing their evolution. The tools include an incremental stream clustering algorithm, interactive techniques for controlling its operation, a dynamic map display showing the current situation, and displays for investigating the cluster evolution (time line and space-time cube).Source: Machine Learning and Knowledge Discovery in Databases. European Conference, pp. 316–319, Porto, Potugal, 07-11/09/2015
DOI: 10.1007/978-3-319-23461-8_38
Project(s): CIMPLEX
Metrics:
Andrienko N., Andrienko G., Fuchs G., Rinzivillo S., Betz H. D.
We demonstrate a system of tools for real-time detection of significant clusters of spatial events and observing their evolution. The tools include an incremental stream clustering algorithm, interactive techniques for controlling its operation, a dynamic map display showing the current situation, and displays for investigating the cluster evolution (time line and space-time cube).Source: Machine Learning and Knowledge Discovery in Databases. European Conference, pp. 316–319, Porto, Potugal, 07-11/09/2015
DOI: 10.1007/978-3-319-23461-8_38
Project(s): CIMPLEX
Metrics:
See at:
City Research Online | ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2023
Journal article
Open Access
Benchmarking and survey of explanation methods for black box models
Bodria F., Giannotti F., Guidotti R., Naretto F., Pedreschi D., Rinzivillo S.
The rise of sophisticated black-box machine learning models in Artificial Intelligence systems has prompted the need for explanation methods that reveal how these models work in an understandable way to users and decision makers. Unsurprisingly, the state-of-the-art exhibits currently a plethora of explainers providing many different types of explanations. With the aim of providing a compass for researchers and practitioners, this paper proposes a categorization of explanation methods from the perspective of the type of explanation they return, also considering the different input data formats. The paper accounts for the most representative explainers to date, also discussing similarities and discrepancies of returned explanations through their visual appearance. A companion website to the paper is provided as a continuous update to new explainers as they appear. Moreover, a subset of the most robust and widely adopted explainers, are benchmarked with respect to a repertoire of quantitative metrics.Source: Data mining and knowledge discovery (2023): 1719–1778. doi:10.1007/s10618-023-00933-9
DOI: 10.1007/s10618-023-00933-9
DOI: 10.48550/arxiv.2102.13076
Project(s): TAILOR, HumanE-AI-Net , XAI , SoBigData-PlusPlus
Metrics:
Bodria F., Giannotti F., Guidotti R., Naretto F., Pedreschi D., Rinzivillo S.
The rise of sophisticated black-box machine learning models in Artificial Intelligence systems has prompted the need for explanation methods that reveal how these models work in an understandable way to users and decision makers. Unsurprisingly, the state-of-the-art exhibits currently a plethora of explainers providing many different types of explanations. With the aim of providing a compass for researchers and practitioners, this paper proposes a categorization of explanation methods from the perspective of the type of explanation they return, also considering the different input data formats. The paper accounts for the most representative explainers to date, also discussing similarities and discrepancies of returned explanations through their visual appearance. A companion website to the paper is provided as a continuous update to new explainers as they appear. Moreover, a subset of the most robust and widely adopted explainers, are benchmarked with respect to a repertoire of quantitative metrics.Source: Data mining and knowledge discovery (2023): 1719–1778. doi:10.1007/s10618-023-00933-9
DOI: 10.1007/s10618-023-00933-9
DOI: 10.48550/arxiv.2102.13076
Project(s): TAILOR
, HumanE-AI-Net , XAI , SoBigData-PlusPlus Metrics:
See at:
Data Mining and Knowledge Discovery | link.springer.com | ISTI Repository | doi.org | CNR ExploRA
2023
Contribution to journal
Open Access
Explainable AI. Introduction to the Special Theme
Veerappa M., Rinzivillo S.
Source: ERCIM news online edition 134 (2023): 8–9.
Veerappa M., Rinzivillo S.
Source: ERCIM news online edition 134 (2023): 8–9.
See at:
ercim-news.ercim.eu | ISTI Repository | CNR ExploRA
2007
Contribution to book
Unknown
Knowledge Discovery from Geographical Data
Rinzivillo S., Turini F., Bogorny V., Komer C., Kuijpers B., May M.
During the last decade, data miners became aware of geographical data. Today, knowledge discovery from geographic data is still an open research field but promises to be a solid starting point for developing solutions for mining spatiotemporal patterns in a knowledge-rich territory. As many concepts of geographic feature extraction and data mining are not commonly known within the data mining community, but need to be understood before advancing to spatiotemporal data mining, this chapter provides an introduction to basic concepts of knowledge discovery from geographical data.Source: Mobility, Data Mining and Privacy: Geographic Knowledge Discovery, pp. 243–266. Berlin: Springer-Verlag, 2007
Rinzivillo S., Turini F., Bogorny V., Komer C., Kuijpers B., May M.
During the last decade, data miners became aware of geographical data. Today, knowledge discovery from geographic data is still an open research field but promises to be a solid starting point for developing solutions for mining spatiotemporal patterns in a knowledge-rich territory. As many concepts of geographic feature extraction and data mining are not commonly known within the data mining community, but need to be understood before advancing to spatiotemporal data mining, this chapter provides an introduction to basic concepts of knowledge discovery from geographical data.Source: Mobility, Data Mining and Privacy: Geographic Knowledge Discovery, pp. 243–266. Berlin: Springer-Verlag, 2007
See at: CNR ExploRA
2010
Contribution to book
Open Access
Spatio-temporal clustering
Kisilevich S., Mansmann F., Nanni M., Rinzivillo S.
Spatio-temporal clustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively new subfield of data mining which gained high popularity especially in geographic information sciences due to the pervasiveness of all kinds of location-based or environmental devices that record position, time or/and environmental properties of an object or set of objects in real-time. As a consequence, different types and large amounts of spatio-temporal data became available that introduce new challenges to data analysis and require novel approaches to knowledge discovery. In this chapter we concentrate on the spatio-temporal clustering in geographic space. First, we provide a classification of different types of spatio-temporal data. Then, we focus on one type of spatio-temporal clustering - trajectory clustering, provide an overview of the state-of-the-art approaches and methods of spatio-temporal clustering and finally present several scenarios in different application domains such as movement, cellular networks and environmental studies.Source: Data Mining and Knowledge Discovery Handbook, pp. 855–874. New York: Springer, 2010
DOI: 10.1007/978-0-387-09823-4_44
Metrics:
Kisilevich S., Mansmann F., Nanni M., Rinzivillo S.
Spatio-temporal clustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively new subfield of data mining which gained high popularity especially in geographic information sciences due to the pervasiveness of all kinds of location-based or environmental devices that record position, time or/and environmental properties of an object or set of objects in real-time. As a consequence, different types and large amounts of spatio-temporal data became available that introduce new challenges to data analysis and require novel approaches to knowledge discovery. In this chapter we concentrate on the spatio-temporal clustering in geographic space. First, we provide a classification of different types of spatio-temporal data. Then, we focus on one type of spatio-temporal clustering - trajectory clustering, provide an overview of the state-of-the-art approaches and methods of spatio-temporal clustering and finally present several scenarios in different application domains such as movement, cellular networks and environmental studies.Source: Data Mining and Knowledge Discovery Handbook, pp. 855–874. New York: Springer, 2010
DOI: 10.1007/978-0-387-09823-4_44
Metrics:
See at:
kops.uni-konstanz.de | doi.org | www.springerlink.com | CNR ExploRA
2010
Report
Open Access
Spatio-temporal clustering: a survey
Kisilevich S., Mansmann F., Nanni M., Rinzivillo S.
Spatio-temporal clustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively new sub-field of data mining which gained high popularity especially in geographic information sciences due to the pervasiveness of all kinds of location-based or environmental devices that record position, time or/and environmental properties of an object or set of objects in real-time. As a consequence, different types and large amounts of spatio-temporal data became available that introduce new challenges to data analysis and require novel approaches to knowledge discovery. In this chapter we concentrate on the spatio-temporal clustering in geographic space. First, we provide a classification of different types of spatio-temporal data. Then, we focus on one type of spatio-temporal clustering - trajectory clustering, provide an overview of the state-of-the-art approaches and methods of spatio-temporal clustering and finally present several scenarios in different application domains such as movement, cellular networks and environmental studies.Source: ISTI Technical reports, 2010
Kisilevich S., Mansmann F., Nanni M., Rinzivillo S.
Spatio-temporal clustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively new sub-field of data mining which gained high popularity especially in geographic information sciences due to the pervasiveness of all kinds of location-based or environmental devices that record position, time or/and environmental properties of an object or set of objects in real-time. As a consequence, different types and large amounts of spatio-temporal data became available that introduce new challenges to data analysis and require novel approaches to knowledge discovery. In this chapter we concentrate on the spatio-temporal clustering in geographic space. First, we provide a classification of different types of spatio-temporal data. Then, we focus on one type of spatio-temporal clustering - trajectory clustering, provide an overview of the state-of-the-art approaches and methods of spatio-temporal clustering and finally present several scenarios in different application domains such as movement, cellular networks and environmental studies.Source: ISTI Technical reports, 2010
See at:
ISTI Repository | CNR ExploRA
2011
Contribution to book
Open Access
Who/where are my new customers?
Rinzivillo Salvatore, Ruggieri Salvatore
We present a knowledge discovery case study on customer classification having the objective of mining the distinctive characteristics of new customers of a service of tax return. Two general approaches are described. The first one, a symbolic approach, is based on extracting and ranking classification rules on the basis of significativeness measures defined on the 4-fold contingency table of a rule. The second one, a spatial approach, is based on extracting geographic areas with predominant presence of new customers.Source: Emerging Intelligent Technologies in Industry, edited by Dominik Ry?ko, Henryk Rybi?ski, Piotr Gawrysiak, Marzena Kryszkiewicz, pp. 307. Berlin/Heidelberg: Springer-Verlag, 2011
DOI: 10.1007/978-3-642-22732-5_25
Metrics:
Rinzivillo Salvatore, Ruggieri Salvatore
We present a knowledge discovery case study on customer classification having the objective of mining the distinctive characteristics of new customers of a service of tax return. Two general approaches are described. The first one, a symbolic approach, is based on extracting and ranking classification rules on the basis of significativeness measures defined on the 4-fold contingency table of a rule. The second one, a spatial approach, is based on extracting geographic areas with predominant presence of new customers.Source: Emerging Intelligent Technologies in Industry, edited by Dominik Ry?ko, Henryk Rybi?ski, Piotr Gawrysiak, Marzena Kryszkiewicz, pp. 307. Berlin/Heidelberg: Springer-Verlag, 2011
DOI: 10.1007/978-3-642-22732-5_25
Metrics:
See at:
www.di.unipi.it | doi.org | link.springer.com | CNR ExploRA
2013
Conference article
Unknown
Privacy-aware distributed mobility data analytics
Pratesi F., Monreale A., Wang H., Rinzivillo S., Pedreschi D., Andrienko G., Andrienko N.
We propose an approach to preserve privacy in an analytical process- ing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by in- dividual vehicles and shipped to a central server. Movement data are sensitive because they may describe typical movement behaviors and therefore be used for re-identification of individuals in a database. We provide a privacy-preserving framework for movement data aggregation based on trajectory generalization in a distributed environment. The proposed solution, based on the differential pri- vacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the ef- fectiveness of our approach also in terms of data utility preserved by the data transformation.Source: SEBD 2013 - 21st Italian Symposium on Advanced Database Systems, Roccella Jonica, Reggio Calabria, Italy, 30 June - 3 July 2013
Project(s): LIFT
Pratesi F., Monreale A., Wang H., Rinzivillo S., Pedreschi D., Andrienko G., Andrienko N.
We propose an approach to preserve privacy in an analytical process- ing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by in- dividual vehicles and shipped to a central server. Movement data are sensitive because they may describe typical movement behaviors and therefore be used for re-identification of individuals in a database. We provide a privacy-preserving framework for movement data aggregation based on trajectory generalization in a distributed environment. The proposed solution, based on the differential pri- vacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the ef- fectiveness of our approach also in terms of data utility preserved by the data transformation.Source: SEBD 2013 - 21st Italian Symposium on Advanced Database Systems, Roccella Jonica, Reggio Calabria, Italy, 30 June - 3 July 2013
Project(s): LIFT
See at: CNR ExploRA
2014
Contribution to book
Restricted
Evaluation of spatio-temporal microsimulation systems
Pappalardo L., Rinzivillo S., Christine K., Kochan B., May M., Schulz D., Simini F.
The increasing expressiveness of spatio-temporal microsimulation systems makes them attractive for a wide range of real world applications. However, the broad field of applications puts new challenges to the quality of microsimulation systems. They are no longer expected to reflect a few selected mobility characteristics but to be a realistic representation of the real world. In consequence, the validation of spatio-temporal microsimulations has to be deepened and to be especially moved towards a holistic view on movement validation. One advantage hereby is the easier availability of mobility data sets at present, which enables to validate many different aspects of movement behavior. However, these data sets bring their own challenges as the data may cover only a part of the observation space, differ in its temporal resolution or be not representative in all aspects. In addition, the definition of appropriate similarity measures, which capture the various mobility characteristics. The goal of this chapter is to pave the way for a novel, better and more detailed evaluation standard for spatio-temporal microsimulation systems. The chapter collects and structures various aspects that have to be considered for the validation and comparison of movement data. In addition, it assembles the state-of-the-art of existing validation techniques. It concludes with examples of using big data sources for the extraction and validation of movement characteristics outlining the research challenges that have yet to be conquered.Source: Data Science and Simulation in Transportation Research, edited by Davy Janssens, Ansar-Ul-Haque Yasar, Luk Knapen, pp. 141–146. Hershey: IGI Global, 2014
DOI: 10.4018/978-1-4666-4920-0.ch008
Metrics:
Pappalardo L., Rinzivillo S., Christine K., Kochan B., May M., Schulz D., Simini F.
The increasing expressiveness of spatio-temporal microsimulation systems makes them attractive for a wide range of real world applications. However, the broad field of applications puts new challenges to the quality of microsimulation systems. They are no longer expected to reflect a few selected mobility characteristics but to be a realistic representation of the real world. In consequence, the validation of spatio-temporal microsimulations has to be deepened and to be especially moved towards a holistic view on movement validation. One advantage hereby is the easier availability of mobility data sets at present, which enables to validate many different aspects of movement behavior. However, these data sets bring their own challenges as the data may cover only a part of the observation space, differ in its temporal resolution or be not representative in all aspects. In addition, the definition of appropriate similarity measures, which capture the various mobility characteristics. The goal of this chapter is to pave the way for a novel, better and more detailed evaluation standard for spatio-temporal microsimulation systems. The chapter collects and structures various aspects that have to be considered for the validation and comparison of movement data. In addition, it assembles the state-of-the-art of existing validation techniques. It concludes with examples of using big data sources for the extraction and validation of movement characteristics outlining the research challenges that have yet to be conquered.Source: Data Science and Simulation in Transportation Research, edited by Davy Janssens, Ansar-Ul-Haque Yasar, Luk Knapen, pp. 141–146. Hershey: IGI Global, 2014
DOI: 10.4018/978-1-4666-4920-0.ch008
Metrics:
See at:
doi.org | CNR ExploRA
2013
Report
Unknown
Privacy-by-design in big data analytics and social mining
Monreale A., Rinzivillo S., Pratesi F., Giannotti F., Pedreschi D.
Privacy is ever-growing concern in our society: the lack of reliable privacy safeguards in many current services and devices is the basis of a diffusion that is often more limited than expected. Moreover, people feel reluctant to provide true personal data, unless it is absolutely necessary. Thus, privacy is becoming a fundamental aspect to take into account when one wants to use, publish and analyze data involving sensitive information. Unfortunately, it is increasingly hard to transform the data in a way that it protects sensitive information: we live in the era of big data characterized by unprecedented opportunities to sense, store and analyze social data describing human activities in great detail and resolution. As a result privacy preservation simply cannot be accomplished by de-identification. In this paper, we propose the privacy-by-design paradigm to develop technological frameworks for countering the threats of undesirable, unlawful effects of privacy violation, without obstructing the knowledge discovery opportunities of social mining and big data analytical technologies. Our main idea is to inscribe privacy protection into the knowledge discovery technology by design, so that the analysis incorporates the relevant privacy requirements from the start.Source: ISTI Technical reports, 2013
Monreale A., Rinzivillo S., Pratesi F., Giannotti F., Pedreschi D.
Privacy is ever-growing concern in our society: the lack of reliable privacy safeguards in many current services and devices is the basis of a diffusion that is often more limited than expected. Moreover, people feel reluctant to provide true personal data, unless it is absolutely necessary. Thus, privacy is becoming a fundamental aspect to take into account when one wants to use, publish and analyze data involving sensitive information. Unfortunately, it is increasingly hard to transform the data in a way that it protects sensitive information: we live in the era of big data characterized by unprecedented opportunities to sense, store and analyze social data describing human activities in great detail and resolution. As a result privacy preservation simply cannot be accomplished by de-identification. In this paper, we propose the privacy-by-design paradigm to develop technological frameworks for countering the threats of undesirable, unlawful effects of privacy violation, without obstructing the knowledge discovery opportunities of social mining and big data analytical technologies. Our main idea is to inscribe privacy protection into the knowledge discovery technology by design, so that the analysis incorporates the relevant privacy requirements from the start.Source: ISTI Technical reports, 2013
See at: CNR ExploRA
2013
Contribution to book
Unknown
Car traffic monitoring
Janssens D., Nanni M., Rinzivillo S.
Source: Mobility Data - Modeling, Management, and Understanding, edited by Chiara Renso, Stefano Spaccapietra, Esteban Zimányi, pp. 197–220, 2013
Janssens D., Nanni M., Rinzivillo S.
Source: Mobility Data - Modeling, Management, and Understanding, edited by Chiara Renso, Stefano Spaccapietra, Esteban Zimányi, pp. 197–220, 2013
See at: CNR ExploRA
2014
Contribution to book
Restricted
From tweets to semantic trajectories: mining anomalous urban mobility patterns
Gabrielli L., Rinzivillo S., Ronzano F., Villatoro D.
This paper proposes and experiments new techniques to detect urban mobility patterns and anomalies by analyzing trajectories mined from publicly available geo-positioned social media traces left by the citizens (namely Twitter). By collecting a large set of geo-located tweets characterizing a specific urban area over time, we semantically enrich the available tweets with information about its author - i.e. a res- ident or a tourist - and the purpose of the movement - i.e. the activity performed in each place. We exploit mobility data mining techniques together with social net- work analysis methods to aggregate similar trajectories thus pointing out hot spots of activities and flows of people together with their varia- tions over time. We apply and validate the proposed trajectory mining approaches to a large set of trajectories built from the geo-positioned tweets gathered in Barcelona during the Mobile World Congress 2012 (MWC2012), one of the greatest events that affected the city in 2012.Source: Citizen in Sensor Networks, edited by Jordi Nin, Daniel Villatoro, pp. 26–35. London: Springer, 2014
DOI: 10.1007/978-3-319-04178-0_3
Project(s): DATA SIM
Metrics:
Gabrielli L., Rinzivillo S., Ronzano F., Villatoro D.
This paper proposes and experiments new techniques to detect urban mobility patterns and anomalies by analyzing trajectories mined from publicly available geo-positioned social media traces left by the citizens (namely Twitter). By collecting a large set of geo-located tweets characterizing a specific urban area over time, we semantically enrich the available tweets with information about its author - i.e. a res- ident or a tourist - and the purpose of the movement - i.e. the activity performed in each place. We exploit mobility data mining techniques together with social net- work analysis methods to aggregate similar trajectories thus pointing out hot spots of activities and flows of people together with their varia- tions over time. We apply and validate the proposed trajectory mining approaches to a large set of trajectories built from the geo-positioned tweets gathered in Barcelona during the Mobile World Congress 2012 (MWC2012), one of the greatest events that affected the city in 2012.Source: Citizen in Sensor Networks, edited by Jordi Nin, Daniel Villatoro, pp. 26–35. London: Springer, 2014
DOI: 10.1007/978-3-319-04178-0_3
Project(s): DATA SIM
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2007
Journal article
Restricted
Knowledge discovery from spatial transactions
Rinzivillo S., Turini F.
We propose a general mechanism to represent the spatial transactions in a way that allows the use of the existing data mining methods. Our proposal allows the analyst to exploit the layered structure of geographical information systems in order to define the layers of interest and the relevant spatial relations among them. Given a reference object, it is possible to describe its neighborhood by considering the attribute of the object itself and the objects related by the chosen relations. The resulting spatial transactions may be either considered like "traditional" transactions, by considering only the qualitative spatial relations, or their spatial extension can be exploited during the data mining process. We explore both these cases. First we tackle the problem of classifying a spatial dataset, by taking into account the spatial component of the data to compute the statistical measure (i.e., the entropy) necessary to learn the model. Then, we consider the task of extracting spatial association rules, by focusing on the qualitative representation of the spatial relations. The feasibility of the process has been tested by implementing the proposed method on top of a GIS tool and by analyzing real world data. © Springer Science+Business Media, LLC 2007.Source: Journal of intelligent information systems 28 (2007): 1–22. doi:10.1007/s10844-006-0001-4
DOI: 10.1007/s10844-006-0001-4
Metrics:
Rinzivillo S., Turini F.
We propose a general mechanism to represent the spatial transactions in a way that allows the use of the existing data mining methods. Our proposal allows the analyst to exploit the layered structure of geographical information systems in order to define the layers of interest and the relevant spatial relations among them. Given a reference object, it is possible to describe its neighborhood by considering the attribute of the object itself and the objects related by the chosen relations. The resulting spatial transactions may be either considered like "traditional" transactions, by considering only the qualitative spatial relations, or their spatial extension can be exploited during the data mining process. We explore both these cases. First we tackle the problem of classifying a spatial dataset, by taking into account the spatial component of the data to compute the statistical measure (i.e., the entropy) necessary to learn the model. Then, we consider the task of extracting spatial association rules, by focusing on the qualitative representation of the spatial relations. The feasibility of the process has been tested by implementing the proposed method on top of a GIS tool and by analyzing real world data. © Springer Science+Business Media, LLC 2007.Source: Journal of intelligent information systems 28 (2007): 1–22. doi:10.1007/s10844-006-0001-4
DOI: 10.1007/s10844-006-0001-4
Metrics:
See at:
Journal of Intelligent Information Systems | CNR ExploRA