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
2010
Other
Unknown
FP7-FET CA MODAP - Mobility Data Mining and Privacy
Giannotti F., Renso C., Rinzivillo S.
Progetto Coordination Action su Mobility, data Mining and Privacy
Giannotti F., Renso C., Rinzivillo S.
Progetto Coordination Action su Mobility, data Mining and Privacy
See at: CNR ExploRA
2010
Software
Unknown
M-Atlas - Atlas of the Urban Mobility
Trasarti R., Pinelli F., Rinzivillo S.
A software tool for analysis mobility data.
Trasarti R., Pinelli F., Rinzivillo S.
A software tool for analysis mobility data.
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
2007
Other
Unknown
GeoPKDD - Geographic Knowledge Discovery and Delivery
Giannotti F., Nanni M., Renso C., Rinzivillo S.
The goal of the GeoPKDD project is to develop theory, techniques and systems for geographic knowledge discovery, based on new privacy-preserving methods for extracting knowledge from large amounts of raw data referenced in space and time.
Giannotti F., Nanni M., Renso C., Rinzivillo S.
The goal of the GeoPKDD project is to develop theory, techniques and systems for geographic knowledge discovery, based on new privacy-preserving methods for extracting knowledge from large amounts of raw data referenced in space and time.
See at: CNR ExploRA
2014
Contribution to book
Restricted
Mobility profiling
Nanni M., Trasarti R., Cintia P., Furletti B., Gabrielli L., Rinzivillo S., Giannotti F.
An abstract is not availableSource: Data Science and Simulation in Transportation Research, edited by Davy Janssens, Ansar-Ul-Haque Yasar, Luk Knapen, pp. 1–29. Hershey: IGI Global, 2014
DOI: 10.4018/978-1-4666-4920-0.ch001
Metrics:
Nanni M., Trasarti R., Cintia P., Furletti B., Gabrielli L., Rinzivillo S., Giannotti F.
An abstract is not availableSource: Data Science and Simulation in Transportation Research, edited by Davy Janssens, Ansar-Ul-Haque Yasar, Luk Knapen, pp. 1–29. Hershey: IGI Global, 2014
DOI: 10.4018/978-1-4666-4920-0.ch001
Metrics:
See at:
www.igi-global.com 
| www.igi-global.com | CNR ExploRA
2012
Report
Unknown
Analisi di mobilità con dati eterogenei
Furletti B., Trasarti R., Gabrielli L., Rinzivillo S., Pappalardo L., Giannotti F.
Technical report about mobility data analysis, studies and experiments in Tuscany, by using mobility data, as: variable message signs data, gps and gsm data, and demographic data. These analysis and methods are the results of several projects and researches of KDDLab.Source: ISTI Technical reports, 2012
Furletti B., Trasarti R., Gabrielli L., Rinzivillo S., Pappalardo L., Giannotti F.
Technical report about mobility data analysis, studies and experiments in Tuscany, by using mobility data, as: variable message signs data, gps and gsm data, and demographic data. These analysis and methods are the results of several projects and researches of KDDLab.Source: ISTI Technical reports, 2012
See at: CNR ExploRA
2011
Report
Open Access
IPERMOB - Specifiche Funzionali OO4
Gennaro C., Amato G., Costalli L., Nanni M., Rinzivillo S., Vairo C., Gabrielli L., Zedda M.
This deliverable provides a high-level description of the functionalities offered by WP4. In particular it provides the API and the REST web service specification for accessing and receiving the data from the sensors.Source: Project report, IPERMOB, Deliverable D1.4, 2011
Gennaro C., Amato G., Costalli L., Nanni M., Rinzivillo S., Vairo C., Gabrielli L., Zedda M.
This deliverable provides a high-level description of the functionalities offered by WP4. In particular it provides the API and the REST web service specification for accessing and receiving the data from the sensors.Source: Project report, IPERMOB, Deliverable D1.4, 2011
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
2014
Report
Unknown
Valutazione del rischio di privacy nel processo di costruzione dei modelli di call habit che sottostanno al sociometro = Assessing the Privacy Risk in the Process of Building Call Habit Models that Underlie the Sociometer
Furletti B., Gabrielli L., Monreale A., Nanni M., Pratesi F., Rinzivillo S., Giannotti F., Pedreschi D.
The paper discusses in detail the problem of the privacy of the users of the original phone data, demonstrating the possibility to measure the risk of identification from the compact representation of the profiles.Source: ISTI Technical reports, 2014
Furletti B., Gabrielli L., Monreale A., Nanni M., Pratesi F., Rinzivillo S., Giannotti F., Pedreschi D.
The paper discusses in detail the problem of the privacy of the users of the original phone data, demonstrating the possibility to measure the risk of identification from the compact representation of the profiles.Source: ISTI Technical reports, 2014
See at: 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
2022
Conference article
Open Access
Exemplars and counterexemplars explanations for skin lesion classifiers
Metta C., Guidotti R., Yin Y., Gallinari P., Rinzivillo S.
Explainable AI consists in developing models allowing interaction between decision systems and humans by making the decisions understandable. We propose a case study for skin lesion diagnosis showing how it is possible to provide explanations of the decisions of deep neural network trained to label skin lesions.Source: HHAI2022 - Augmenting Human Intellect, pp. 258–260, Amsterdam, The Netherlands, 13-17/07/2022
DOI: 10.3233/faia220209
Project(s): HumanE-AI-Net
Metrics:
Metta C., Guidotti R., Yin Y., Gallinari P., Rinzivillo S.
Explainable AI consists in developing models allowing interaction between decision systems and humans by making the decisions understandable. We propose a case study for skin lesion diagnosis showing how it is possible to provide explanations of the decisions of deep neural network trained to label skin lesions.Source: HHAI2022 - Augmenting Human Intellect, pp. 258–260, Amsterdam, The Netherlands, 13-17/07/2022
DOI: 10.3233/faia220209
Project(s): HumanE-AI-Net
Metrics:
See at:
ebooks.iospress.nl | ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Trustworthy AI at KDD Lab
Giannotti F., Guidotti R., Monreale A., Pappalardo L., Pedreschi D., Pellungrini R., Pratesi F., Rinzivillo S., Ruggieri S., Setzu M., Deluca R.
This document summarizes the activities regarding the development of Responsible AI (Responsible Artificial Intelligence) conducted by the Knowledge Discovery and Data mining group (KDD-Lab), a joint research group of the Institute of Information Science and Technologies "Alessandro Faedo" (ISTI) of the National Research Council of Italy (CNR), the Department of Computer Science of the University of Pisa, and the Scuola Normale Superiore of Pisa.Source: Ital-IA 2023, pp. 388–393, Pisa, Italy, 29-30/05/2023
Project(s): SoBigData-PlusPlus
Giannotti F., Guidotti R., Monreale A., Pappalardo L., Pedreschi D., Pellungrini R., Pratesi F., Rinzivillo S., Ruggieri S., Setzu M., Deluca R.
This document summarizes the activities regarding the development of Responsible AI (Responsible Artificial Intelligence) conducted by the Knowledge Discovery and Data mining group (KDD-Lab), a joint research group of the Institute of Information Science and Technologies "Alessandro Faedo" (ISTI) of the National Research Council of Italy (CNR), the Department of Computer Science of the University of Pisa, and the Scuola Normale Superiore of Pisa.Source: Ital-IA 2023, pp. 388–393, Pisa, Italy, 29-30/05/2023
Project(s): SoBigData-PlusPlus
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2011
Report
Open Access
Types and operators in M-Atlas system
Trasarti Roberto, Rinzivillo Salvatore, Nanni Mirco, Giannotti Fosca
In this technical report we illustrate the types managed by the M-Atlas system and the operators defined among them. Due to the complexity of spatio-temporal data, models and patterns, the system is be based on a rich formalism, capable to representing the specificity of movement data. We choose the object-relational model, which combines the simplicity of the relational data model and SQL with the basic object oriented capabilities. The main feature of the object- relational database model is that objects and classes are directly supported in database schemas supporting the extension of the original types with custom types representing complex structures. Moreover using a pre-existing GIS technology developed on the database the new types and operators can be integrated easily.Source: ISTI Technical reports, 2011
Trasarti Roberto, Rinzivillo Salvatore, Nanni Mirco, Giannotti Fosca
In this technical report we illustrate the types managed by the M-Atlas system and the operators defined among them. Due to the complexity of spatio-temporal data, models and patterns, the system is be based on a rich formalism, capable to representing the specificity of movement data. We choose the object-relational model, which combines the simplicity of the relational data model and SQL with the basic object oriented capabilities. The main feature of the object- relational database model is that objects and classes are directly supported in database schemas supporting the extension of the original types with custom types representing complex structures. Moreover using a pre-existing GIS technology developed on the database the new types and operators can be integrated easily.Source: ISTI Technical reports, 2011
See at:
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
Conference article
Unknown
A generalisation-based approach to anonymising movement data
Andrienko G., Andrienko N., Giannotti F., Monreale A., Pedreschi D., Rinzivillo S.
The possibility to collect, store, disseminate, and analyze data about movements of people raises very serious privacy concerns, given the sensitivity of the information about personal positions. In particular, sensitive information about individuals can be uncovered with the use of data mining and visual analytics methods. In this paper we present a method for the generalization of trajectory data that can be adopted as the first step of a process to obtain k-anonymity in spatio-temporal datasets. We ran a preliminary set of experiments on a real-world trajectory dataset, demonstrating that this method of generalization of trajectories preserves the clustering analysis results.Source: The 13th AGILE conference on Geographic Information Science, Guimarães, Portugal, 10-14 May 2010
Andrienko G., Andrienko N., Giannotti F., Monreale A., Pedreschi D., Rinzivillo S.
The possibility to collect, store, disseminate, and analyze data about movements of people raises very serious privacy concerns, given the sensitivity of the information about personal positions. In particular, sensitive information about individuals can be uncovered with the use of data mining and visual analytics methods. In this paper we present a method for the generalization of trajectory data that can be adopted as the first step of a process to obtain k-anonymity in spatio-temporal datasets. We ran a preliminary set of experiments on a real-world trajectory dataset, demonstrating that this method of generalization of trajectories preserves the clustering analysis results.Source: The 13th AGILE conference on Geographic Information Science, Guimarães, Portugal, 10-14 May 2010
See at: CNR ExploRA
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
2012
Journal article
Restricted
Data science for simulating the era of electric vehicles
Janssens D., Giannotti F., Nanni M., Pedreschi D., Rinzivillo S.
The vision and scientific challenges presented in this paper are the objectives of the FET-FP7 project DATASIM. The project aims at providing an entirely new and highly detailed spatio-temporal microsimula- tion methodology for human mobility, grounded on mas- sive amounts of big data of various types and from var- ious sources, with the goal to forecast the nation-wide consequences of a massive switch to electric vehicles, given the intertwined nature of mobility and power dis- tribution networks.Source: KI. Künstliche Intelligenz (Oldenbourg) 26 (2012): 275–278. doi:10.1007/s13218-012-0183-6
DOI: 10.1007/s13218-012-0183-6
Project(s): DATA SIM
Metrics:
Janssens D., Giannotti F., Nanni M., Pedreschi D., Rinzivillo S.
The vision and scientific challenges presented in this paper are the objectives of the FET-FP7 project DATASIM. The project aims at providing an entirely new and highly detailed spatio-temporal microsimula- tion methodology for human mobility, grounded on mas- sive amounts of big data of various types and from var- ious sources, with the goal to forecast the nation-wide consequences of a massive switch to electric vehicles, given the intertwined nature of mobility and power dis- tribution networks.Source: KI. Künstliche Intelligenz (Oldenbourg) 26 (2012): 275–278. doi:10.1007/s13218-012-0183-6
DOI: 10.1007/s13218-012-0183-6
Project(s): DATA SIM
Metrics:
See at:
KI - Künstliche Intelligenz | link.springer.com | CNR ExploRA
2013
Conference article
Unknown
Validating general human mobility patterns on massive GPS data
Pappalardo L., Rinzivillo S., Pedreschi D., Giannotti F.
Are the patterns of car travel dierent from those of general human mobility? Based on a unique dataset consisting of the GPS tra- jectories of 10 million travels accomplished by 150,000 cars in Italy, we investigate how known mobility models apply to car travels, and illus- trate novel analytical ndings. We also assess to what extent the sample in our dataset is representative of the overall car mobility, and discover how to build an extremely accurate model that, given our GPS data, estimates the real trac values as measured by road sensors.Source: SEBD 2013 - 21st Italian Symposium on Advanced Database Systems, pp. 305–312, Università di Reggio Calabria, 30 June - 03 July 2013
Pappalardo L., Rinzivillo S., Pedreschi D., Giannotti F.
Are the patterns of car travel dierent from those of general human mobility? Based on a unique dataset consisting of the GPS tra- jectories of 10 million travels accomplished by 150,000 cars in Italy, we investigate how known mobility models apply to car travels, and illus- trate novel analytical ndings. We also assess to what extent the sample in our dataset is representative of the overall car mobility, and discover how to build an extremely accurate model that, given our GPS data, estimates the real trac values as measured by road sensors.Source: SEBD 2013 - 21st Italian Symposium on Advanced Database Systems, pp. 305–312, Università di Reggio Calabria, 30 June - 03 July 2013
See at: CNR ExploRA
2013
Journal article
Closed Access
Understanding the patterns of car travel
Pappalardo L., Rinzivillo S., Qu Z., Pedreschi D., Giannotti F.
Are the patterns of car travel different from those of general human mobility? Based on a unique dataset consisting of the GPS trajectories of 10 million travels accomplished by 150,000 cars in Italy, we investigate how known mobility models apply to car travels, and illustrate novel analytical findings. We also assess to what extent the sample in our dataset is representative of the overall car mobility, and discover how to build an extremely accurate model that, given our GPS data, estimates the real traffic values as measured by road sensors.Source: The European physical journal. Special topics 215 (2013): 61–73. doi:10.1140/epjst/e2013-01715-5
DOI: 10.1140/epjst/e2013-01715-5
Metrics:
Pappalardo L., Rinzivillo S., Qu Z., Pedreschi D., Giannotti F.
Are the patterns of car travel different from those of general human mobility? Based on a unique dataset consisting of the GPS trajectories of 10 million travels accomplished by 150,000 cars in Italy, we investigate how known mobility models apply to car travels, and illustrate novel analytical findings. We also assess to what extent the sample in our dataset is representative of the overall car mobility, and discover how to build an extremely accurate model that, given our GPS data, estimates the real traffic values as measured by road sensors.Source: The European physical journal. Special topics 215 (2013): 61–73. doi:10.1140/epjst/e2013-01715-5
DOI: 10.1140/epjst/e2013-01715-5
Metrics:
See at:
The European Physical Journal Special Topics | link.springer.com | CNR ExploRA