Showing 1–19 of 19 results for author: Nanni, M

An Explainable Failure Prediction Framework for Neural Networks in Radio Access Networks

Authors: Khaleda Papry, Francesco Spinnato, Marco Fiore, Mirco Nanni, Israat Haque

Abstract: As 5G networks continue to evolve to deliver high speed, low latency, and reliable communications, ensuring uninterrupted service has become increasingly critical. While millimeter wave (mmWave) frequencies enable gigabit data rates, they are highly susceptible to environmental factors, often leading to radio link failures (RLF). Predictive models leveraging radio and weather data have been propos… ▽ More As 5G networks continue to evolve to deliver high speed, low latency, and reliable communications, ensuring uninterrupted service has become increasingly critical. While millimeter wave (mmWave) frequencies enable gigabit data rates, they are highly susceptible to environmental factors, often leading to radio link failures (RLF). Predictive models leveraging radio and weather data have been proposed to address this issue; however, many operate as black boxes, offering limited transparency for operational deployment. This work bridges that gap by introducing a framework that combines explainability based feature pruning with model refinement. Our framework can be integrated into state of the art predictors such as GNN Transformer and LSTM based architectures for RLF prediction, enabling the development of accurate and explainability guided models in 5G networks. It provides insights into the contribution of input features and the decision making logic of neural networks, leading to lighter and more scalable models. When applied to RLF prediction, our framework unveils that weather data contributes minimally to the forecast in extensive real world datasets, which informs the design of a leaner model with 50 percent fewer parameters and improved F1 scores with respect to the state of the art solution. Ultimately, this work empowers network providers to evaluate and refine their neural network based prediction models for better interpretability, scalability, and performance. △ Less

Submitted 28 January, 2026; originally announced February 2026.

Data-driven Exploration of Mobility Interaction Patterns

Authors: Gabriele Galatolo, Mirco Nanni

Abstract: Understanding the movement behaviours of individuals and the way they react to the external world is a key component of any problem that involves the modelling of human dynamics at a physical level. In particular, it is crucial to capture the influence that the presence of an individual can have on the others. Important examples of applications include crowd simulation and emergency management, wh… ▽ More Understanding the movement behaviours of individuals and the way they react to the external world is a key component of any problem that involves the modelling of human dynamics at a physical level. In particular, it is crucial to capture the influence that the presence of an individual can have on the others. Important examples of applications include crowd simulation and emergency management, where the simulation of the mass of people passes through the simulation of the individuals, taking into consideration the others as part of the general context. While existing solutions basically start from some preconceived behavioural model, in this work we propose an approach that starts directly from the data, adopting a data mining perspective. Our method searches the mobility events in the data that might be possible evidences of mutual interactions between individuals, and on top of them looks for complex, persistent patterns and time evolving configurations of events. The study of these patterns can provide new insights on the mechanics of mobility interactions between individuals, which can potentially help in improving existing simulation models. We instantiate the general methodology on two real case studies, one on cars and one on pedestrians, and a full experimental evaluation is performed, both in terms of performances, parameter sensitivity and interpretation of sample results. △ Less

Submitted 8 December, 2025; originally announced December 2025.

PruneGCRN: Minimizing and explaining spatio-temporal problems through node pruning

Authors: Javier García-Sigüenza, Mirco Nanni, Faraón Llorens-Largo, José F. Vicent

Abstract: This work addresses the challenge of using a deep learning model to prune graphs and the ability of this method to integrate explainability into spatio-temporal problems through a new approach. Instead of applying explainability to the model's behavior, we seek to gain a better understanding of the problem itself. To this end, we propose a novel model that integrates an optimized pruning mechanism… ▽ More This work addresses the challenge of using a deep learning model to prune graphs and the ability of this method to integrate explainability into spatio-temporal problems through a new approach. Instead of applying explainability to the model's behavior, we seek to gain a better understanding of the problem itself. To this end, we propose a novel model that integrates an optimized pruning mechanism capable of removing nodes from the graph during the training process, rather than doing so as a separate procedure. This integration allows the architecture to learn how to minimize prediction error while selecting the most relevant nodes. Thus, during training, the model searches for the most relevant subset of nodes, obtaining the most important elements of the problem, facilitating its analysis. To evaluate the proposed approach, we used several widely used traffic datasets, comparing the accuracy obtained by pruning with the model and with other methods. The experiments demonstrate that our method is capable of retaining a greater amount of information as the graph reduces in size compared to the other methods used. These results highlight the potential of pruning as a tool for developing models capable of simplifying spatio-temporal problems, thereby obtaining their most important elements. △ Less

Submitted 12 October, 2025; originally announced October 2025.

A computational framework for quantifying route diversification in road networks

Authors: Giuliano Cornacchia, Luca Pappalardo, Mirco Nanni, Dino Pedreschi, Marta C. González

Abstract: The structure of road networks impacts various urban dynamics, from traffic congestion to environmental sustainability and access to essential services. Recent studies reveal that most roads are underutilized, faster alternative routes are often overlooked, and traffic is typically concentrated on a few corridors. In this article, we examine how road network structure, and in particular the presen… ▽ More The structure of road networks impacts various urban dynamics, from traffic congestion to environmental sustainability and access to essential services. Recent studies reveal that most roads are underutilized, faster alternative routes are often overlooked, and traffic is typically concentrated on a few corridors. In this article, we examine how road network structure, and in particular the presence of mobility attractors (e.g., highways and ring roads), shapes the counterpart to traffic concentration: route diversification. To this end, we introduce DiverCity, a measure that quantifies the extent to which traffic can potentially be distributed across multiple, loosely overlapping near-shortest routes. Analyzing 56 diverse global cities, we find that DiverCity is influenced by network characteristics and is associated with traffic efficiency. Within cities, DiverCity increases with distance from the city center before stabilizing in the periphery, but declines in the proximity of mobility attractors. We demonstrate that strategic speed limit adjustments on mobility attractors can increase DiverCity while preserving travel efficiency. We isolate the complex interplay between mobility attractors and DiverCity through simulations in a controlled setting, confirming the patterns observed in real-world cities. DiverCity provides a practical tool for urban planners and policymakers to optimize road network design and balance route diversification, efficiency, and sustainability. We provide an interactive platform (https://divercitymaps.github.io) to visualize the spatial distribution of DiverCity across all considered cities. △ Less

Submitted 2 October, 2025; originally announced October 2025.

The Path is the Goal: a Study on the Nature and Effects of Shortest-Path Stability Under Perturbation of Destination

Authors: Giuliano Cornacchia, Mirco Nanni

Abstract: This work examines the phenomenon of path variability in urban navigation, where small changes in destination might lead to significantly different suggested routes. Starting from an observation of this variability over the city of Barcelona, we explore whether this is a localized or widespread occurrence and identify factors influencing path variability. We introduce the concept of "path stabilit… ▽ More This work examines the phenomenon of path variability in urban navigation, where small changes in destination might lead to significantly different suggested routes. Starting from an observation of this variability over the city of Barcelona, we explore whether this is a localized or widespread occurrence and identify factors influencing path variability. We introduce the concept of "path stability", a measure of how robust a suggested route is to minor destination adjustments, define a detailed experimentation process and apply it across multiple cities worldwide. Our analysis shows that path stability is shaped by city-specific factors and trip characteristics, also identifying some common patterns. Results reveal significant heterogeneity in path stability across cities, allowing for categorization into "stable" and "unstable" cities. These findings offer new insights for urban planning and traffic management, highlighting opportunities for optimizing navigation systems to enhance route consistency and urban mobility. △ Less

Submitted 11 June, 2025; originally announced June 2025.

Navigation services amplify concentration of traffic and emissions in our cities

Authors: Giuliano Cornacchia, Mirco Nanni, Dino Pedreschi, Luca Pappalardo

Abstract: The proliferation of human-AI ecosystems involving human interaction with algorithms, such as assistants and recommenders, raises concerns about large-scale social behaviour. Despite evidence of such phenomena across several contexts, the collective impact of GPS navigation services remains unclear: while beneficial to the user, they can also cause chaos if too many vehicles are driven through the… ▽ More The proliferation of human-AI ecosystems involving human interaction with algorithms, such as assistants and recommenders, raises concerns about large-scale social behaviour. Despite evidence of such phenomena across several contexts, the collective impact of GPS navigation services remains unclear: while beneficial to the user, they can also cause chaos if too many vehicles are driven through the same few roads. Our study employs a simulation framework to assess navigation services' influence on road network usage and CO2 emissions. The results demonstrate a universal pattern of amplified conformity: increasing adoption rates of navigation services cause a reduction of route diversity of mobile travellers and increased concentration of traffic and emissions on fewer roads, thus exacerbating an unequal distribution of negative externalities on selected neighbourhoods. Although navigation services recommendations can help reduce CO2 emissions when their adoption rate is low, these benefits diminish or even disappear when the adoption rate is high and exceeds a certain city- and service-dependent threshold. We summarize these discoveries in a non-linear function that connects the marginal increase of conformity with the marginal reduction in CO2 emissions. Our simulation approach addresses the challenges posed by the complexity of transportation systems and the lack of data and algorithmic transparency. △ Less

Submitted 29 July, 2024; originally announced July 2024.

A survey on the impacts of recommender systems on users, items, and human-AI ecosystems

Authors: Luca Pappalardo, Salvatore Citraro, Giuliano Cornacchia, Mirco Nanni, Valentina Pansanella, Giulio Rossetti, Gizem Gezici, Fosca Giannotti, Margherita Lalli, Giovanni Mauro, Gabriele Barlacchi, Daniele Gambetta, Virginia Morini, Dino Pedreschi, Emanuele Ferragina

Abstract: Recommendation systems and assistants (in short, recommenders) influence through online platforms most actions of our daily lives, suggesting items or providing solutions based on users' preferences or requests. This survey systematically reviews, categories, and discusses the impact of recommenders in four human-AI ecosystems -- social media, online retail, urban mapping and generative AI ecosyst… ▽ More Recommendation systems and assistants (in short, recommenders) influence through online platforms most actions of our daily lives, suggesting items or providing solutions based on users' preferences or requests. This survey systematically reviews, categories, and discusses the impact of recommenders in four human-AI ecosystems -- social media, online retail, urban mapping and generative AI ecosystems. Its scope is to systematise a fast-growing field in which terminologies employed to classify methodologies and outcomes are fragmented and unsystematic. This is a crucial contribution to the literature because terminologies vary substantially across disciplines and ecosystems, hindering comparison and accumulation of knowledge in the field. We follow the customary steps of qualitative systematic review, gathering 154 articles from different disciplines to develop a parsimonious taxonomy of methodologies employed (empirical, simulation, observational, controlled), outcomes observed (concentration, content degradation, discrimination, diversity, echo chamber, filter bubble, homogenisation, polarisation, radicalisation, volume), and their level of analysis (individual, item, and ecosystem). We systematically discuss substantive and methodological commonalities across ecosystems, and highlight potential avenues for future research. The survey is addressed to scholars and practitioners interested in different human-AI ecosystems, policymakers and institutional stakeholders who want to understand better the measurable outcomes of recommenders, and tech companies who wish to obtain a systematic view of the impact of their recommenders. △ Less

Submitted 10 December, 2025; v1 submitted 29 June, 2024; originally announced July 2024.

A Bag of Receptive Fields for Time Series Extrinsic Predictions

Authors: Francesco Spinnato, Riccardo Guidotti, Anna Monreale, Mirco Nanni

Abstract: High-dimensional time series data poses challenges due to its dynamic nature, varying lengths, and presence of missing values. This kind of data requires extensive preprocessing, limiting the applicability of existing Time Series Classification and Time Series Extrinsic Regression techniques. For this reason, we propose BORF, a Bag-Of-Receptive-Fields model, which incorporates notions from time se… ▽ More High-dimensional time series data poses challenges due to its dynamic nature, varying lengths, and presence of missing values. This kind of data requires extensive preprocessing, limiting the applicability of existing Time Series Classification and Time Series Extrinsic Regression techniques. For this reason, we propose BORF, a Bag-Of-Receptive-Fields model, which incorporates notions from time series convolution and 1D-SAX to handle univariate and multivariate time series with varying lengths and missing values. We evaluate BORF on Time Series Classification and Time Series Extrinsic Regression tasks using the full UEA and UCR repositories, demonstrating its competitive performance against state-of-the-art methods. Finally, we outline how this representation can naturally provide saliency and feature-based explanations. △ Less

Submitted 29 November, 2023; originally announced November 2023.

One-Shot Traffic Assignment with Forward-Looking Penalization

Authors: Giuliano Cornacchia, Mirco Nanni, Luca Pappalardo

Abstract: Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider real-time traffic conditions, resulting in inefficient route assignments. This paper introduces METIS, a cooperative, one-shot TA algorithm that combines alternative routing with edge penalization and… ▽ More Traffic assignment (TA) is crucial in optimizing transportation systems and consists in efficiently assigning routes to a collection of trips. Existing TA algorithms often do not adequately consider real-time traffic conditions, resulting in inefficient route assignments. This paper introduces METIS, a cooperative, one-shot TA algorithm that combines alternative routing with edge penalization and informed route scoring. We conduct experiments in several cities to evaluate the performance of METIS against state-of-the-art one-shot methods. Compared to the best baseline, METIS significantly reduces CO2 emissions by 18% in Milan, 28\% in Florence, and 46% in Rome, improving trip distribution considerably while still having low computational time. Our study proposes METIS as a promising solution for optimizing TA and urban transportation systems. △ Less

Submitted 23 June, 2023; originally announced June 2023.

Journal ref: SIGSPATIAL 2023: Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems

Modeling Events and Interactions through Temporal Processes -- A Survey

Authors: Angelica Liguori, Luciano Caroprese, Marco Minici, Bruno Veloso, Francesco Spinnato, Mirco Nanni, Giuseppe Manco, Joao Gama

Abstract: In real-world scenario, many phenomena produce a collection of events that occur in continuous time. Point Processes provide a natural mathematical framework for modeling these sequences of events. In this survey, we investigate probabilistic models for modeling event sequences through temporal processes. We revise the notion of event modeling and provide the mathematical foundations that characte… ▽ More In real-world scenario, many phenomena produce a collection of events that occur in continuous time. Point Processes provide a natural mathematical framework for modeling these sequences of events. In this survey, we investigate probabilistic models for modeling event sequences through temporal processes. We revise the notion of event modeling and provide the mathematical foundations that characterize the literature on the topic. We define an ontology to categorize the existing approaches in terms of three families: simple, marked, and spatio-temporal point processes. For each family, we systematically review the existing approaches based based on deep learning. Finally, we analyze the scenarios where the proposed techniques can be used for addressing prediction and modeling aspects. △ Less

Submitted 21 July, 2023; v1 submitted 10 March, 2023; originally announced March 2023.

Comments: Image replacements

How Routing Strategies Impact Urban Emissions

Authors: Giuliano Cornacchia, Matteo Böhm, Giovanni Mauro, Mirco Nanni, Dino Pedreschi, Luca Pappalardo

Abstract: Navigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., carbon dioxide emissions and population exposure to pollution) is still largely unclear. In this work, we design a simulation framework to assess the impact of routing algorithms on carbon dioxide emissions withi… ▽ More Navigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., carbon dioxide emissions and population exposure to pollution) is still largely unclear. In this work, we design a simulation framework to assess the impact of routing algorithms on carbon dioxide emissions within an urban environment. Using APIs from TomTom and OpenStreetMap, we find that settings in which either all vehicles or none of them follow a navigation app's suggestion lead to the worst impact in terms of CO2 emissions. In contrast, when just a portion (around half) of vehicles follow these suggestions, and some degree of randomness is added to the remaining vehicles' paths, we observe a reduction in the overall CO2 emissions over the road network. Our work is a first step towards designing next-generation routing principles that may increase urban well-being while satisfying individual needs. △ Less

Submitted 4 July, 2022; originally announced July 2022.

Journal ref: SIGSPATIAL 2022: Proceedings of the 30th International Conference on Advances in Geographic Information Systems

Gross polluters and vehicles' emissions reduction

Authors: Matteo Böhm, Mirco Nanni, Luca Pappalardo

Abstract: Vehicles' emissions produce a significant share of cities' air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing vehicles' full driving cycle, or focus on a few vehicles. We use GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of private vehicles in… ▽ More Vehicles' emissions produce a significant share of cities' air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing vehicles' full driving cycle, or focus on a few vehicles. We use GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of private vehicles in three European cities. We find that the emissions across the vehicles and roads are well approximated by heavy-tailed distributions and thus discover the existence of gross polluters, vehicles responsible for the greatest quantity of emissions, and grossly polluted roads, which suffer the greatest amount of emissions. Our simulations show that emissions reduction policies targeting gross polluters are way more effective than those limiting circulation based on a non-informed choice of vehicles. Our study contributes to shaping the discussion on how to measure emissions with digital data. △ Less

Submitted 17 March, 2022; v1 submitted 21 April, 2021; originally announced July 2021.

Comments: Version to be published in Nature Sustainability. Minor changes due to the last round of reviews

Journal ref: Nat. Sustain. (2022)

Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks

Authors: Gevorg Yeghikyan, Felix L. Opolka, Mirco Nanni, Bruno Lepri, Pietro Lio'

Abstract: A fundamental problem of interest to policy makers, urban planners, and other stakeholders involved in urban development projects is assessing the impact of planning and construction activities on mobility flows. This is a challenging task due to the different spatial, temporal, social, and economic factors influencing urban mobility flows. These flows, along with the influencing factors, can be m… ▽ More A fundamental problem of interest to policy makers, urban planners, and other stakeholders involved in urban development projects is assessing the impact of planning and construction activities on mobility flows. This is a challenging task due to the different spatial, temporal, social, and economic factors influencing urban mobility flows. These flows, along with the influencing factors, can be modelled as attributed graphs with both node and edge features characterising locations in a city and the various types of relationships between them. In this paper, we address the problem of assessing origin-destination (OD) car flows between a location of interest and every other location in a city, given their features and the structural characteristics of the graph. We propose three neural network architectures, including graph neural networks (GNN), and conduct a systematic comparison between the proposed methods and state-of-the-art spatial interaction models, their modifications, and machine learning approaches. The objective of the paper is to address the practical problem of estimating potential flow between an urban development project location and other locations in the city, where the features of the project location are known in advance. We evaluate the performance of the models on a regression task using a custom data set of attributed car OD flows in London. We also visualise the model performance by showing the spatial distribution of flow residuals across London. △ Less

Submitted 24 April, 2020; originally announced April 2020.

Comments: 9 pages, 5 figures, to be published in the Proceedings of 2020 IEEE International Conference on Smart Computing (SMARTCOMP 2020)

Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown

Authors: Pietro Bonato, Paolo Cintia, Francesco Fabbri, Daniele Fadda, Fosca Giannotti, Pier Luigi Lopalco, Sara Mazzilli, Mirco Nanni, Luca Pappalardo, Dino Pedreschi, Francesco Penone, Salvatore Rinzivillo, Giulio Rossetti, Marcello Savarese, Lara Tavoschi

Abstract: Understanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the epidemics. In this report, we use mobile phone data to infer the movements of people between Italian provinces and municipalities, and we analyze the incoming, outcoming and internal mobility flows before and during the n… ▽ More Understanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the epidemics. In this report, we use mobile phone data to infer the movements of people between Italian provinces and municipalities, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modelling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. We address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak? △ Less

Submitted 23 April, 2020; originally announced April 2020.

Give more data, awareness and control to individual citizens, and they will help COVID-19 containment

Authors: Mirco Nanni, Gennady Andrienko, Albert-László Barabási, Chiara Boldrini, Francesco Bonchi, Ciro Cattuto, Francesca Chiaromonte, Giovanni Comandé, Marco Conti, Mark Coté, Frank Dignum, Virginia Dignum, Josep Domingo-Ferrer, Paolo Ferragina, Fosca Giannotti, Riccardo Guidotti, Dirk Helbing, Kimmo Kaski, Janos Kertesz, Sune Lehmann, Bruno Lepri, Paul Lukowicz, Stan Matwin, David Megías Jiménez, Anna Monreale , et al. (14 additional authors not shown)

Abstract: The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countri… ▽ More The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively, voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates - if and when they want, for specific aims - with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society. △ Less

Submitted 16 April, 2020; v1 submitted 10 April, 2020; originally announced April 2020.

Comments: Revised text. Additional authors

Journal ref: Transactions on Data Privacy 13(1): 61-66 (2020), http://www.tdp.cat/issues16/abs.a389a20.php

A Cross-Entropy-based Method to Perform Information-based Feature Selection

Authors: Pietro Cassara, Alessandro Rozza, Mirco Nanni

Abstract: From a machine learning point of view, identifying a subset of relevant features from a real data set can be useful to improve the results achieved by classification methods and to reduce their time and space complexity. To achieve this goal, feature selection methods are usually employed. These approaches assume that the data contains redundant or irrelevant attributes that can be eliminated. In… ▽ More From a machine learning point of view, identifying a subset of relevant features from a real data set can be useful to improve the results achieved by classification methods and to reduce their time and space complexity. To achieve this goal, feature selection methods are usually employed. These approaches assume that the data contains redundant or irrelevant attributes that can be eliminated. In this work, we propose a novel algorithm to manage the optimization problem that is at the foundation of the Mutual Information feature selection methods. Furthermore, our novel approach is able to estimate automatically the number of dimensions to retain. The quality of our method is confirmed by the promising results achieved on standard real data sets. △ Less

Submitted 22 May, 2017; v1 submitted 25 July, 2016; originally announced July 2016.

An effective Time-Aware Map Matching process for low sampling GPS data

Authors: Paolo Cintia, Mirco Nanni

Abstract: In the era of the proliferation of Geo-Spatial Data, induced by the diffusion of GPS devices, the map matching problem still represents an important and valuable challenge. The process of associating a segment of the underlying road network to a GPS point gives us the chance to enrich raw data with the semantic layer provided by the roadmap, with all contextual information associated to it, e.g. t… ▽ More In the era of the proliferation of Geo-Spatial Data, induced by the diffusion of GPS devices, the map matching problem still represents an important and valuable challenge. The process of associating a segment of the underlying road network to a GPS point gives us the chance to enrich raw data with the semantic layer provided by the roadmap, with all contextual information associated to it, e.g. the presence of speed limits, attraction points, changes in elevation, etc. Most state-of-art solutions for this classical problem simply look for the shortest or fastest path connecting any pair of consecutive points in a trip. While in some contexts that is reasonable, in this work we argue that the shortest/fastest path assumption can be in general erroneous. Indeed, we show that such approaches can yield travel times that are significantly incoherent with the real ones, and propose a Time-Aware Map matching process that tries to improve the state-of-art by taking into account also such temporal aspect. Our algorithm results to be very efficient, effective on low- sampling data and to outperform existing solutions, as proved by experiments on large datasets of real GPS trajectories. Moreover, our algorithm is parameter-free and does not depend on specific characteristics of the GPS localization error and of the road network (e.g. density of roads, road network topology, etc.). △ Less

Submitted 23 March, 2016; originally announced March 2016.

The Inductive Constraint Programming Loop

Authors: Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen, Barry O'Sullivan, Anastasia Paparrizou, Dino Pedreschi, Helmut Simonis

Abstract: Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, that we call the Inductive Const… ▽ More Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, that we call the Inductive Constraint Programming loop. In this approach data is gathered and analyzed systematically, in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other hand. △ Less

Submitted 12 October, 2015; originally announced October 2015.

Comments: 17 pages, 9 figures

Probabilistic Agent Programs

Authors: Juergen Dix, Mirco Nanni, VS Subrahmanian

Abstract: Agents are small programs that autonomously take actions based on changes in their environment or ``state.'' Over the last few years, there have been an increasing number of efforts to build agents that can interact and/or collaborate with other agents. In one of these efforts, Eiter, Subrahmanian amd Pick (AIJ, 108(1-2), pages 179-255) have shown how agents may be built on top of legacy code. H… ▽ More Agents are small programs that autonomously take actions based on changes in their environment or ``state.'' Over the last few years, there have been an increasing number of efforts to build agents that can interact and/or collaborate with other agents. In one of these efforts, Eiter, Subrahmanian amd Pick (AIJ, 108(1-2), pages 179-255) have shown how agents may be built on top of legacy code. However, their framework assumes that agent states are completely determined, and there is no uncertainty in an agent's state. Thus, their framework allows an agent developer to specify how his agents will react when the agent is 100% sure about what is true/false in the world state. In this paper, we propose the concept of a \emph{probabilistic agent program} and show how, given an arbitrary program written in any imperative language, we may build a declarative ``probabilistic'' agent program on top of it which supports decision making in the presence of uncertainty. We provide two alternative semantics for probabilistic agent programs. We show that the second semantics, though more epistemically appealing, is more complex to compute. We provide sound and complete algorithms to compute the semantics of \emph{positive} agent programs. △ Less

Submitted 21 October, 1999; originally announced October 1999.

Comments: 44 pages, 1 figure, Appendix

ACM Class: D.1.6