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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

See at: CNR ExploRA


2010 Other Unknown

M-Atlas - Atlas of the Urban Mobility
Trasarti R., Pinelli F., Rinzivillo S.
A software tool for analysis mobility data.

See at: CNR ExploRA


2013 Part of book or chapter of book Restricted

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

See at: CNR ExploRA Restricted


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.

See at: CNR ExploRA


2014 Part of book or chapter of 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

See at: CNR ExploRA Restricted | www.igi-global.com Restricted | Unknown Repository Restricted


2012 Report Closed Access

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

See at: CNR ExploRA Restricted


2011 Report Open Access OPEN

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

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2011 Part of book or chapter of book Open Access OPEN

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

See at: Unknown Repository Open Access | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | CNR ExploRA Restricted | Unknown Repository Restricted | Unknown Repository Restricted


2014 Report Restricted

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

See at: CNR ExploRA Restricted


2015 Conference object Open Access OPEN

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 via OpenAIRE

See at: Unknown Repository Open Access | City Research Online Open Access | ISTI Repository Open Access | Fraunhofer-ePrints Open Access | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | CNR ExploRA Restricted | Unknown Repository Restricted


2011 Report Open Access OPEN

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

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2007 Part of book or chapter of book Restricted

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

See at: CNR ExploRA Restricted


2010 Conference object Open Access OPEN

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

See at: CNR ExploRA Open Access


2011 Conference object Restricted

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

See at: CNR ExploRA Restricted


2012 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 via OpenAIRE

See at: KI - Künstliche Intelligenz Restricted | KI - Künstliche Intelligenz Restricted | KI - Künstliche Intelligenz Restricted | KI - Künstliche Intelligenz Restricted | link.springer.com Restricted | KI - Künstliche Intelligenz Restricted | KI - Künstliche Intelligenz Restricted | KI - Künstliche Intelligenz Restricted | CNR ExploRA Restricted


2013 Conference object Restricted

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

See at: CNR ExploRA Restricted


2013 Article Restricted

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

See at: The European Physical Journal Special Topics Restricted | The European Physical Journal Special Topics Restricted | The European Physical Journal Special Topics Restricted | The European Physical Journal Special Topics Restricted | The European Physical Journal Special Topics Restricted | The European Physical Journal Special Topics Restricted | The European Physical Journal Special Topics Restricted | link.springer.com | CNR ExploRA


2013 Conference object Restricted

The origin of human heterogeneity: analyzing mobility behavior through GSM and GPS data
Pappalardo L., Simini F., Rinzivillo S., Pedreschi D., Giannotti F.
In the present work, we present a macro-micro analysis of human mobility conducted on two dierent datasets. The rst one is a GSM dataset collected by a European mobile phone carrier for billing and operational purposes. It contains date, time and coordinates of the phone tower routing the communication for each call and text message sent or received by 91; 000 customers, in a period of 3 months. The other one is a GPS dataset storing information of approximately 9; 8 Million dierent car travels from 159; 000 cars tracked during one month (May 2011) in an area corresponding to central Italy.Source: ECCS 2013 - European Conference on Complex Systems, Barcellona, Spain, 16-20 September 2013

See at: CNR ExploRA Restricted | www.eccs13.eu Restricted


2011 Report Restricted

DATA SIM - Semantic-enriched data-driven theory of mobility demand and final framework for integration (D2.2)
Giannotti F., Andrienko G., Andrienko N., Furletti B., Kertesz J., Liu F., Nanni M., Pappalardo L., Pelekis N., Renso C., Rinzivillo S.
This report presents the work pursued during the second year of the project by the partners participating in the Workpackage 2. The document propose an abstract simulation pipeline to discuss how the methods and patterns investigated in the WP may be integrated in a new generation of agent-based simulation systems.Source: Project report, DATA SIM, Deliverable D2.2, 2011
Project(s): DATA SIM via OpenAIRE

See at: CNR ExploRA Restricted


2015 Part of book or chapter of book Restricted

Use of mobile phone data to estimate visitors mobility flows
Gabrielli L., Furletti B., Giannotti F., Nanni M., Rinzivillo S.
Big Data originating from the digital breadcrumbs of human activities, sensed as by-product of the technologies that we use for our daily activities, allows us to observe the individual and collective behavior of people at an unprecedented detail. Many dimensions of our social life have big data "proxies", such as the mobile calls data for mobility. In this paper we investigate to what extent data coming from mobile operators could be a support in producing reliable and timely estimates of intra-city mobility flows. The idea is to define an estimation method based on calling data to characterize the mobility habits of visitors at the level of a single municipality.Source: Software Engineering and Formal Methods, edited by Carlos Canal, Akram Idani, pp. 214–226, 2015
DOI: 10.1007/978-3-319-15201-1_14
Project(s): DATA SIM via OpenAIRE

See at: Unknown Repository Restricted | Unknown Repository Restricted | link.springer.com Restricted | Unknown Repository Restricted | Unknown Repository Restricted | CNR ExploRA Restricted | Unknown Repository Restricted | Unknown Repository Restricted