2011
Conference article
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From movement tracks through events to places: extracting and characterizing significant places from mobility data
Andrienko G, Andrienko N, Hurter C, Rinzivillo S, Wroebel SWe 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.
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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 SPlace-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), vol. 19 (issue 7), pp. 1078-1094
DOI: 10.1109/tvcg.2012.311Metrics:
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IEEE Transactions on Visualization and Computer Graphics
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| Fraunhofer-ePrints
2016
Journal article
Open Access
Leveraging spatial abstraction in traffic analysis and forecasting with visual analytics
Andrienko N, Andrienko G, Rinzivillo SA 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, vol. 57, pp. 172-194
DOI: 10.1016/j.is.2015.08.007Project(s): CIMPLEX 
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| Information Systems
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2015
Conference article
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Detection, tracking, and visualization of spatial event clusters for real time monitoring
Andrienko N, Andrienko G, Fuchs G, Rinzivillo S, Betz HSpatial 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.DOI: 10.1109/dsaa.2015.7344880Project(s): SoBigData
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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 SThe 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, vol. 37, pp. 1719-1778
DOI: 10.1007/s10618-023-00933-9DOI: 10.48550/arxiv.2102.13076Project(s): TAILOR 
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Data Mining and Knowledge Discovery
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2007
Contribution to book
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Knowledge Discovery from Geographical Data
Rinzivillo S, Turini F, Bogorny V, Komer C, Kuijpers B, May MDuring 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.
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2013
Conference article
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Privacy-aware distributed mobility data analytics
Pratesi F, Monreale A, Wang H, Rinzivillo S, Pedreschi D, Andrienko G, Andrienko NWe 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.Project(s): LIFT 
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2013
Contribution to book
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Privacy-preserving Distributed Movement Data Aggregation
Monreale A, Wang Wh, Pratesi F, Rinzivillo S, Pedreschi D, Andrienko G, Andrienko NWe 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: LECTURE NOTES IN GEOINFORMATION AND CARTOGRAPHY, pp. 225-245
DOI: 10.1007/978-3-319-00615-4_13Project(s): DATA SIM
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2014
Contribution to book
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Evaluation of spatio-temporal microsimulation systems
Pappalardo L, Rinzivillo S, Christine K, Kochan B, May M, Schulz D, Simini FThe 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.DOI: 10.4018/978-1-4666-4920-0.ch008Metrics:
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2013
Other
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Differential privacy in distributed mobility analytics
Monreale A, Wang Wh, Pratesi F, Rinzivillo S, Pedreschi D, Andrienko G, Andrienko NMovement 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.
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