TY - CONF T1 - Connected Vehicle Simulation Framework for Parking Occupancy Prediction (Demo Paper) T2 - Proceedings of the 30th International Conference on Advances in Geographic Information Systems Y1 - 2022 A1 - Resce, Pierpaolo A1 - Vorwerk, Lukas A1 - Han, Zhiwei A1 - Cornacchia, Giuliano A1 - Alamdari, Omid Isfahani A1 - Mirco Nanni A1 - Luca Pappalardo A1 - Weimer, Daniel A1 - Liu, Yuanting AB - This paper demonstrates a simulation framework that collects data about connected vehicles' locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications. JF - Proceedings of the 30th International Conference on Advances in Geographic Information Systems PB - Association for Computing Machinery CY - New York, NY, USA SN - 9781450395298 UR - https://doi.org/10.1145/3557915.3560995 ER - TY - CONF T1 - How Routing Strategies Impact Urban Emissions T2 - Proceedings of the 30th International Conference on Advances in Geographic Information Systems Y1 - 2022 A1 - Cornacchia, Giuliano A1 - Böhm, Matteo A1 - Giovanni Mauro A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Luca Pappalardo AB - 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., CO2 emissions and 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. JF - Proceedings of the 30th International Conference on Advances in Geographic Information Systems PB - Association for Computing Machinery CY - New York, NY, USA SN - 9781450395298 UR - https://doi.org/10.1145/3557915.3560977 ER - TY - JOUR T1 - Give more data, awareness and control to individual citizens, and they will help COVID-19 containment Y1 - 2021 A1 - Mirco Nanni A1 - Andrienko, Gennady A1 - Barabasi, Albert-Laszlo A1 - Boldrini, Chiara A1 - Bonchi, Francesco A1 - Cattuto, Ciro A1 - Chiaromonte, Francesca A1 - Comandé, Giovanni A1 - Conti, Marco A1 - Coté, Mark A1 - Dignum, Frank A1 - Dignum, Virginia A1 - Domingo-Ferrer, Josep A1 - Ferragina, Paolo A1 - Fosca Giannotti A1 - Riccardo Guidotti A1 - Helbing, Dirk A1 - Kaski, Kimmo A1 - Kertész, János A1 - Lehmann, Sune A1 - Lepri, Bruno A1 - Lukowicz, Paul A1 - Matwin, Stan A1 - Jiménez, David Megías A1 - Anna Monreale A1 - Morik, Katharina A1 - Oliver, Nuria A1 - Passarella, Andrea A1 - Passerini, Andrea A1 - Dino Pedreschi A1 - Pentland, Alex A1 - Pianesi, Fabio A1 - Francesca Pratesi A1 - S Rinzivillo A1 - Salvatore Ruggieri A1 - Siebes, Arno A1 - Torra, Vicenc A1 - Roberto Trasarti A1 - Hoven, Jeroen van den A1 - Vespignani, Alessandro AB - 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 (e.g., with a backend system, but possibly also with other citizens), 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. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. 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 and 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. SN - 1572-8439 UR - https://link.springer.com/article/10.1007/s10676-020-09572-w JO - Ethics and Information Technology ER - TY - ABST T1 - Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown Y1 - 2020 A1 - Pietro Bonato A1 - Paolo Cintia A1 - Francesco Fabbri A1 - Daniele Fadda A1 - Fosca Giannotti A1 - Pier Luigi Lopalco A1 - Sara Mazzilli A1 - Mirco Nanni A1 - Luca Pappalardo A1 - Dino Pedreschi A1 - Francesco Penone A1 - S Rinzivillo A1 - Giulio Rossetti A1 - Marcello Savarese A1 - Lara Tavoschi AB - 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? UR - https://arxiv.org/abs/2004.11278 ER - TY - CONF T1 - Discovering Mobility Functional Areas: A Mobility Data Analysis Approach T2 - International Workshop on Complex Networks Y1 - 2018 A1 - Lorenzo Gabrielli A1 - Daniele Fadda A1 - Giulio Rossetti A1 - Mirco Nanni A1 - Piccinini, Leonardo A1 - Dino Pedreschi A1 - Fosca Giannotti A1 - Patrizia Lattarulo AB - How do we measure the borders of urban areas and therefore decide which are the functional units of the territory? Nowadays, we typically do that just looking at census data, while in this work we aim to identify functional areas for mobility in a completely data-driven way. Our solution makes use of human mobility data (vehicle trajectories) and consists in an agglomerative process which gradually groups together those municipalities that maximize internal vehicular traffic while minimizing external one. The approach is tested against a dataset of trips involving individuals of an Italian Region, obtaining a new territorial division which allows us to identify mobility attractors. Leveraging such partitioning and external knowledge, we show that our method outperforms the state-of-the-art algorithms. Indeed, the outcome of our approach is of great value to public administrations for creating synergies within the aggregations of the territories obtained. JF - International Workshop on Complex Networks PB - Springer UR - https://link.springer.com/chapter/10.1007/978-3-319-73198-8_27 ER - TY - CHAP T1 - How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science T2 - A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years Y1 - 2018 A1 - Amato, G. A1 - Candela, L. A1 - Castelli, D. A1 - Esuli, A. A1 - Falchi, F. A1 - Gennaro, C. A1 - Fosca Giannotti A1 - Anna Monreale A1 - Mirco Nanni A1 - Pagano, P. A1 - Luca Pappalardo A1 - Dino Pedreschi A1 - Francesca Pratesi A1 - Rabitti, F. A1 - S Rinzivillo A1 - Giulio Rossetti A1 - Salvatore Ruggieri A1 - Sebastiani, F. A1 - Tesconi, M. ED - Flesca, Sergio ED - Greco, Sergio ED - Masciari, Elio ED - Saccà, Domenico AB - During the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today. JF - A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years PB - Springer International Publishing CY - Cham SN - 978-3-319-61893-7 UR - https://link.springer.com/chapter/10.1007%2F978-3-319-61893-7_17 ER - TY - CONF T1 - Clustering Individual Transactional Data for Masses of Users T2 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Y1 - 2017 A1 - Riccardo Guidotti A1 - Anna Monreale A1 - Mirco Nanni A1 - Fosca Giannotti A1 - Dino Pedreschi AB - Mining a large number of datasets recording human activities for making sense of individual data is the key enabler of a new wave of personalized knowledge-based services. In this paper we focus on the problem of clustering individual transactional data for a large mass of users. Transactional data is a very pervasive kind of information that is collected by several services, often involving huge pools of users. We propose txmeans, a parameter-free clustering algorithm able to efficiently partitioning transactional data in a completely automatic way. Txmeans is designed for the case where clustering must be applied on a massive number of different datasets, for instance when a large set of users need to be analyzed individually and each of them has generated a long history of transactions. A deep experimentation on both real and synthetic datasets shows the practical effectiveness of txmeans for the mass clustering of different personal datasets, and suggests that txmeans outperforms existing methods in terms of quality and efficiency. Finally, we present a personal cart assistant application based on txmeans JF - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining PB - ACM ER - TY - JOUR T1 - ICON Loop Carpooling Show Case JF - Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach Y1 - 2017 A1 - Mirco Nanni A1 - Lars Kotthoff A1 - Riccardo Guidotti A1 - Barry O'Sullivan A1 - Dino Pedreschi AB - In this chapter we describe a proactive carpooling service that combines induction and optimization mechanisms to maximize the impact of carpooling within a community. The approach autonomously infers the mobility demand of the users through the analysis of their mobility traces (i.e. Data Mining of GPS trajectories) and builds the network of all possible ride sharing opportunities among the users. Then, the maximal set of carpooling matches that satisfy some standard requirements (maximal capacity of vehicles, etc.) is computed through Constraint Programming models, and the resulting matches are proactively proposed to the users. Finally, in order to maximize the expected impact of the service, the probability that each carpooling match is accepted by the users involved is inferred through Machine Learning mechanisms and put in the CP model. The whole process is reiterated at regular intervals, thus forming an instance of the general ICON loop. VL - 10101 UR - https://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf#page=314 ER - TY - JOUR T1 - The Inductive Constraint Programming Loop JF - IEEE Intelligent Systems Y1 - 2017 A1 - Bessiere, Christian A1 - De Raedt, Luc A1 - Tias Guns A1 - Lars Kotthoff A1 - Mirco Nanni A1 - Siegfried Nijssen A1 - Barry O'Sullivan A1 - Paparrizou, Anastasia A1 - Dino Pedreschi A1 - Simonis, Helmut AB - Constraint programming is used for a variety of real-world optimization 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, which 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. ER - TY - JOUR T1 - The Inductive Constraint Programming Loop JF - Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach Y1 - 2017 A1 - Mirco Nanni A1 - Siegfried Nijssen A1 - Barry O'Sullivan A1 - Paparrizou, Anastasia A1 - Dino Pedreschi A1 - Simonis, Helmut AB - Constraint programming is used for a variety of real-world optimization 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 (ICON) 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 end. VL - 10101 UR - https://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf#page=307 ER - TY - CONF T1 - Movement Behaviour Recognition for Water Activities T2 - Personal Analytics and Privacy. An Individual and Collective Perspective - First International Workshop, {PAP} 2017, Held in Conjunction with {ECML} {PKDD} 2017, Skopje, Macedonia, September 18, 2017, Revised Selected Papers Y1 - 2017 A1 - Mirco Nanni A1 - Roberto Trasarti A1 - Fosca Giannotti JF - Personal Analytics and Privacy. An Individual and Collective Perspective - First International Workshop, {PAP} 2017, Held in Conjunction with {ECML} {PKDD} 2017, Skopje, Macedonia, September 18, 2017, Revised Selected Papers UR - https://doi.org/10.1007/978-3-319-71970-2_7 ER - TY - JOUR T1 - Never drive alone: Boosting carpooling with network analysis JF - Information Systems Y1 - 2017 A1 - Riccardo Guidotti A1 - Mirco Nanni A1 - S Rinzivillo A1 - Dino Pedreschi A1 - Fosca Giannotti AB - Carpooling, i.e., the act where two or more travelers share the same car for a common trip, is one of the possibilities brought forward to reduce traffic and its externalities, but experience shows that it is difficult to boost the adoption of carpooling to significant levels. In our study, we analyze the potential impact of carpooling as a collective phenomenon emerging from people׳s mobility, by network analytics. Based on big mobility data from travelers in a given territory, we construct the network of potential carpooling, where nodes correspond to the users and links to possible shared trips, and analyze the structural and topological properties of this network, such as network communities and node ranking, to the purpose of highlighting the subpopulations with higher chances to create a carpooling community, and the propensity of users to be either drivers or passengers in a shared car. Our study is anchored to reality thanks to a large mobility dataset, consisting of the complete one-month-long GPS trajectories of approx. 10% circulating cars in Tuscany. We also analyze the aggregated outcome of carpooling by means of empirical simulations, showing how an assignment policy exploiting the network analytic concepts of communities and node rankings minimizes the number of single occupancy vehicles observed after carpooling. VL - 64 ER - TY - JOUR T1 - Scalable and flexible clustering solutions for mobile phone-based population indicators JF - I. J. Data Science and Analytics Y1 - 2017 A1 - Alessandro Lulli A1 - Lorenzo Gabrielli A1 - Patrizio Dazzi A1 - Matteo Dell'Amico A1 - Pietro Michiardi A1 - Mirco Nanni A1 - Laura Ricci VL - 4 UR - https://doi.org/10.1007/s41060-017-0065-y ER - TY - CONF T1 - There's A Path For Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas T2 - 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017) Y1 - 2017 A1 - Riccardo Guidotti A1 - Roberto Trasarti A1 - Mirco Nanni A1 - Fosca Giannotti A1 - Dino Pedreschi JF - 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017) PB - IEEE CY - Tokyo ER - TY - CONF T1 - Big Data and Public Administration: a case study for Tuscany Airports T2 - SEBD - Italian Symposium on Advanced Database Systems Y1 - 2016 A1 - Barbara Furletti A1 - Daniele Fadda A1 - Leonardo Piccini A1 - Mirco Nanni A1 - Patrizia Lattarulo AB - In the last decade, the fast development of Information and Communication Technologies led to the wide diffusion of sensors able to track various aspects of human activity, as well as the storage and computational capabilities needed to record and analyze them. The so-called Big Data promise to improve the effectiveness of businesses, the quality of urban life, as well as many other fields, including the functioning of public administrations. Yet, translating the wealth of potential information hidden in Big Data to consumable intelligence seems to be still a difficult task, with a limited basis of success stories. This paper reports a project activity centered on a public administration - IRPET, the Regional Institute for Economic Planning of Tuscany (Italy). The paper deals, among other topics, with human mobility and public transportation at a regional scale, summarizing the open questions posed by the Public Administration (PA), the envisioned role that Big Data might have in answering them, the actual challenges that emerged in trying to implement them, and finally the results we obtained, the limitations that emerged and the lessons learned. JF - SEBD - Italian Symposium on Advanced Database Systems PB - Matematicamente.it CY - Ugento, Lecce (Italy) SN - 9788896354889 UR - http://sebd2016.unisalento.it/grid/SEBD2016-proceedings.pdf ER - TY - JOUR T1 - Driving Profiles Computation and Monitoring for Car Insurance CRM JF - Journal ACM Transactions on Intelligent Systems and Technology (TIST) Y1 - 2016 A1 - Mirco Nanni A1 - Roberto Trasarti A1 - Anna Monreale A1 - Valerio Grossi A1 - Dino Pedreschi AB - Customer segmentation is one of the most traditional and valued tasks in customer relationship management (CRM). In this article, we explore the problem in the context of the car insurance industry, where the mobility behavior of customers plays a key role: Different mobility needs, driving habits, and skills imply also different requirements (level of coverage provided by the insurance) and risks (of accidents). In the present work, we describe a methodology to extract several indicators describing the driving profile of customers, and we provide a clustering-oriented instantiation of the segmentation problem based on such indicators. Then, we consider the availability of a continuous flow of fresh mobility data sent by the circulating vehicles, aiming at keeping our segments constantly up to date. We tackle a major scalability issue that emerges in this context when the number of customers is large-namely, the communication bottleneck-by proposing and implementing a sophisticated distributed monitoring solution that reduces communications between vehicles and company servers to the essential. We validate the framework on a large database of real mobility data coming from GPS devices on private cars. Finally, we analyze the privacy risks that the proposed approach might involve for the users, providing and evaluating a countermeasure based on data perturbation. VL - 8 UR - http://doi.acm.org/10.1145/2912148 ER - TY - CHAP T1 - Partition-Based Clustering Using Constraint Optimization T2 - Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach Y1 - 2016 A1 - Valerio Grossi A1 - Tias Guns A1 - Anna Monreale A1 - Mirco Nanni A1 - Siegfried Nijssen AB - Partition-based clustering is the task of partitioning a dataset in a number of groups of examples, such that examples in each group are similar to each other. Many criteria for what constitutes a good clustering have been identified in the literature; furthermore, the use of additional constraints to find more useful clusterings has been proposed. In this chapter, it will be shown that most of these clustering tasks can be formalized using optimization criteria and constraints. We demonstrate how a range of clustering tasks can be modelled in generic constraint programming languages with these constraints and optimization criteria. Using the constraint-based modeling approach we also relate the DBSCAN method for density-based clustering to the label propagation technique for community discovery. JF - Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach PB - Springer International Publishing UR - http://dx.doi.org/10.1007/978-3-319-50137-6_11 ER - TY - CONF T1 - Clustering Formulation Using Constraint Optimization T2 - Software Engineering and Formal Methods - {SEFM} 2015 Collocated Workshops: ATSE, HOFM, MoKMaSD, and VERY*SCART, York, UK, September 7-8, 2015, Revised Selected Papers Y1 - 2015 A1 - Valerio Grossi A1 - Anna Monreale A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Franco Turini AB - The problem of clustering a set of data is a textbook machine learning problem, but at the same time, at heart, a typical optimization problem. Given an objective function, such as minimizing the intra-cluster distances or maximizing the inter-cluster distances, the task is to find an assignment of data points to clusters that achieves this objective. In this paper, we present a constraint programming model for a centroid based clustering and one for a density based clustering. In particular, as a key contribution, we show how the expressivity introduced by the formulation of the problem by constraint programming makes the standard problem easy to be extended with other constraints that permit to generate interesting variants of the problem. We show this important aspect in two different ways: first, we show how the formulation of the density-based clustering by constraint programming makes it very similar to the label propagation problem and then, we propose a variant of the standard label propagation approach. JF - Software Engineering and Formal Methods - {SEFM} 2015 Collocated Workshops: ATSE, HOFM, MoKMaSD, and VERY*SCART, York, UK, September 7-8, 2015, Revised Selected Papers PB - Springer Berlin Heidelberg UR - http://dx.doi.org/10.1007/978-3-662-49224-6_9 ER - TY - CONF T1 - Find Your Way Back: Mobility Profile Mining with Constraints T2 - Principles and Practice of Constraint Programming Y1 - 2015 A1 - Lars Kotthoff A1 - Mirco Nanni A1 - Riccardo Guidotti A1 - Barry O'Sullivan AB - Mobility profile mining is a data mining task that can be formulated as clustering over movement trajectory data. The main challenge is to separate the signal from the noise, i.e. one-off trips. We show that standard data mining approaches suffer the important drawback that they cannot take the symmetry of non-noise trajectories into account. That is, if a trajectory has a symmetric equivalent that covers the same trip in the reverse direction, it should become more likely that neither of them is labelled as noise. We present a constraint model that takes this knowledge into account to produce better clusters. We show the efficacy of our approach on real-world data that was previously processed using standard data mining techniques. JF - Principles and Practice of Constraint Programming PB - Springer International Publishing CY - Cork ER - TY - CONF T1 - {TOSCA:} two-steps clustering algorithm for personal locations detection T2 - Proceedings of the 23rd {SIGSPATIAL} International Conference on Advances in Geographic Information Systems, Bellevue, WA, USA, November 3-6, 2015 Y1 - 2015 A1 - Riccardo Guidotti A1 - Roberto Trasarti A1 - Mirco Nanni JF - Proceedings of the 23rd {SIGSPATIAL} International Conference on Advances in Geographic Information Systems, Bellevue, WA, USA, November 3-6, 2015 UR - http://doi.acm.org/10.1145/2820783.2820818 ER - TY - CONF T1 - Towards user-centric data management: individual mobility analytics for collective services T2 - Proceedings of the 4th {ACM} {SIGSPATIAL} International Workshop on Mobile Geographic Information Systems, MobiGIS 2015, Bellevue, WA, USA, November 3-6, 2015 Y1 - 2015 A1 - Riccardo Guidotti A1 - Roberto Trasarti A1 - Mirco Nanni A1 - Fosca Giannotti JF - Proceedings of the 4th {ACM} {SIGSPATIAL} International Workshop on Mobile Geographic Information Systems, MobiGIS 2015, Bellevue, WA, USA, November 3-6, 2015 UR - http://doi.acm.org/10.1145/2834126.2834132 ER - TY - CHAP T1 - Use of Mobile Phone Data to Estimate Visitors Mobility Flows T2 - Software Engineering and Formal Methods Y1 - 2015 A1 - Lorenzo Gabrielli A1 - Barbara Furletti A1 - Fosca Giannotti A1 - Mirco Nanni A1 - S Rinzivillo AB - 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. JF - Software Engineering and Formal Methods PB - Springer International Publishing VL - 8938 UR - http://link.springer.com/chapter/10.1007%2F978-3-319-15201-1_14 ER - TY - CONF T1 - Big data analytics for smart mobility: a case study T2 - EDBT/ICDT 2014 Workshops - Mining Urban Data (MUD) Y1 - 2014 A1 - Barbara Furletti A1 - Roberto Trasarti A1 - Lorenzo Gabrielli A1 - Mirco Nanni A1 - Dino Pedreschi JF - EDBT/ICDT 2014 Workshops - Mining Urban Data (MUD) CY - Athens, Greece UR - http://ceur-ws.org/Vol-1133/paper-57.pdf ER - TY - JOUR T1 - Discovering urban and country dynamics from mobile phone data with spatial correlation patterns JF - Telecommunications Policy Y1 - 2014 A1 - Roberto Trasarti A1 - Ana-Maria Olteanu-Raimond A1 - Mirco Nanni A1 - Thomas Couronné A1 - Barbara Furletti A1 - Fosca Giannotti A1 - Zbigniew Smoreda A1 - Cezary Ziemlicki KW - Urban dynamics AB - Abstract Mobile communication technologies pervade our society and existing wireless networks are able to sense the movement of people, generating large volumes of data related to human activities, such as mobile phone call records. At the present, this kind of data is collected and stored by telecom operators infrastructures mainly for billing reasons, yet it represents a major source of information in the study of human mobility. In this paper, we propose an analytical process aimed at extracting interconnections between different areas of the city that emerge from highly correlated temporal variations of population local densities. To accomplish this objective, we propose a process based on two analytical tools: (i) a method to estimate the presence of people in different geographical areas; and (ii) a method to extract time- and space-constrained sequential patterns capable to capture correlations among geographical areas in terms of significant co-variations of the estimated presence. The methods are presented and combined in order to deal with two real scenarios of different spatial scale: the Paris Region and the whole France. UR - http://www.sciencedirect.com/science/article/pii/S0308596113002012 ER - TY - CHAP T1 - Mobility Profiling T2 - Data Science and Simulation in Transportation Research Y1 - 2014 A1 - Mirco Nanni A1 - Roberto Trasarti A1 - Paolo Cintia A1 - Barbara Furletti A1 - Chiara Renso A1 - Lorenzo Gabrielli A1 - S Rinzivillo A1 - Fosca Giannotti AB - The ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network. JF - Data Science and Simulation in Transportation Research PB - IGI Global ER - TY - CONF T1 - The purpose of motion: Learning activities from Individual Mobility Networks T2 - International Conference on Data Science and Advanced Analytics, {DSAA} 2014, Shanghai, China, October 30 - November 1, 2014 Y1 - 2014 A1 - S Rinzivillo A1 - Lorenzo Gabrielli A1 - Mirco Nanni A1 - Luca Pappalardo A1 - Dino Pedreschi A1 - Fosca Giannotti JF - International Conference on Data Science and Advanced Analytics, {DSAA} 2014, Shanghai, China, October 30 - November 1, 2014 UR - http://dx.doi.org/10.1109/DSAA.2014.7058090 ER - TY - CONF T1 - Use of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach T2 - 47th SIS Scientific Meeting of the Italian Statistica Society Y1 - 2014 A1 - Barbara Furletti A1 - Lorenzo Gabrielli A1 - Fosca Giannotti A1 - Letizia Milli A1 - Mirco Nanni A1 - Dino Pedreschi AB - The Big Data, originating from the digital breadcrumbs of human activi- ties, sensed as a by-product of the technologies that we use for our daily activities, let us to observe the individual and collective behavior of people at an unprecedented detail. Many dimensions of our social life have big data “proxies”, as the mobile calls data for mobility. In this paper we investigate to what extent such ”big data”, in integration with administrative ones, could be a support in producing reliable and timely estimates of inter-city mobility. The study has been jointly developed by Is- tat, CNR, University of Pisa in the range of interest of the “Commssione di studio avente il compito di orientare le scelte dellIstat sul tema dei Big Data ”. In an on- going project at ISTAT, called “Persons and Places” – based on an integration of administrative data sources, it has been produced a first release of Origin Destina- tion matrix – at municipality level – assuming that the places of residence and that of work (or study) be the terminal points of usual individual mobility for work or study. The coincidence between the city of residence and that of work (or study) – is considered as a proxy of the absence of intercity mobility for a person (we define him a static resident). The opposite case is considered as a proxy of presence of mo- bility (the person is a dynamic resident: commuter or embedded). As administrative data do not contain information on frequency of the mobility, the idea is to specify an estimate method, using calling data as support, to define for each municipality the stock of standing residents, embedded city users and daily city users (commuters) JF - 47th SIS Scientific Meeting of the Italian Statistica Society CY - Cagliari SN - 978-88-8467-874-4 UR - http://www.sis2014.it/proceedings/allpapers/3026.pdf ER - TY - CONF T1 - Use of mobile phone data to estimate visitors mobility flows T2 - Proceedings of MoKMaSD Y1 - 2014 A1 - Lorenzo Gabrielli A1 - Barbara Furletti A1 - Fosca Giannotti A1 - Mirco Nanni A1 - S Rinzivillo AB - 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 mo- bile 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 JF - Proceedings of MoKMaSD UR - http://www.di.unipi.it/mokmasd/symposium-2014/preproceedings/GabrielliEtAl-mokmasd2014.pdf ER - TY - CONF T1 - MP4-A Project: Mobility Planning For Africa T2 - In D4D Challenge @ 3rd Conf. on the Analysis of Mobile Phone datasets (NetMob 2013) Y1 - 2013 A1 - Mirco Nanni A1 - Roberto Trasarti A1 - Barbara Furletti A1 - Lorenzo Gabrielli A1 - Peter Van Der Mede A1 - Joost De Bruijn A1 - Erik de Romph A1 - Gerard Bruil AB - This project aims to create a tool that uses mobile phone transaction (trajectory) data that will be able to address transportation related challenges, thus allowing promotion and facilitation of sustainable urban mobility planning in Third World countries. The proposed tool is a transport demand model for Ivory Coast, with emphasis on its major urbanization Abidjan. The consortium will bring together available data from the internet, and integrate these with the mobility data obtained from the mobile phones in order to build the best possible transport model. A transport model allows an understanding of current and future infrastructure requirements in Ivory Coast. As such, this project will provide the first proof of concept. In this context, long-term analysis of individual call traces will be performed to reconstruct systematic movements, and to infer an origin-destination matrix. A similar process will be performed using the locations of caller and recipient of phone calls, enabling the comparison of socio-economic ties vs. mobility. The emerging links between different areas will be used to build an effective map to optimize regional border definitions and road infrastructure from a mobility perspective. Finally, we will try to build specialized origin-destination matrices for specific categories of population. Such categories will be inferred from data through analysis of calling behaviours, and will also be used to characterize the population of different cities. The project also includes a study of data compliance with distributions of standard measures observed in literature, including distribution of calls, call durations and call network features. JF - In D4D Challenge @ 3rd Conf. on the Analysis of Mobile Phone datasets (NetMob 2013) CY - Cambridge, USA UR - http://perso.uclouvain.be/vincent.blondel/netmob/2013/D4D-book.pdf ER - TY - JOUR T1 - Spatio-Temporal Data JF - Spatio-Temporal Databases: Flexible Querying and Reasoning Y1 - 2013 A1 - Mirco Nanni A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Franco Turini ER - TY - CONF T1 - A Study on Parameter Estimation for a Mining Flock Algorithm T2 - Mining Complex Patterns Workshop, ECML PKDD 2013 Y1 - 2013 A1 - Rebecca Ong A1 - Mirco Nanni A1 - Chiara Renso A1 - Monica Wachowicz A1 - Dino Pedreschi JF - Mining Complex Patterns Workshop, ECML PKDD 2013 ER - TY - CONF T1 - Transportation Planning Based on {GSM} Traces: {A} Case Study on Ivory Coast T2 - Citizen in Sensor Networks - Second International Workshop, CitiSens 2013, Barcelona, Spain, September 19, 2013, Revised Selected Papers Y1 - 2013 A1 - Mirco Nanni A1 - Roberto Trasarti A1 - Barbara Furletti A1 - Lorenzo Gabrielli A1 - Peter Van Der Mede A1 - Joost De Bruijn A1 - Erik de Romph A1 - Gerard Bruil JF - Citizen in Sensor Networks - Second International Workshop, CitiSens 2013, Barcelona, Spain, September 19, 2013, Revised Selected Papers UR - http://dx.doi.org/10.1007/978-3-319-04178-0_2 ER - TY - CONF T1 - An Agent-Based Model to Evaluate Carpooling at Large Manufacturing Plants T2 - Proceedings of the 3rd International Conference on Ambient Systems, Networks and Technologies {(ANT} 2012), the 9th International Conference on Mobile Web Information Systems (MobiWIS-2012), Niagara Falls, Ontario, Canada, August 27-29, 2012 Y1 - 2012 A1 - Tom Bellemans A1 - Sebastian Bothe A1 - Sungjin Cho A1 - Fosca Giannotti A1 - Davy Janssens A1 - Luk Knapen A1 - Christine Körner A1 - Michael May A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Hendrik Stange A1 - Roberto Trasarti A1 - Ansar-Ul-Haque Yasar A1 - Geert Wets JF - Proceedings of the 3rd International Conference on Ambient Systems, Networks and Technologies {(ANT} 2012), the 9th International Conference on Mobile Web Information Systems (MobiWIS-2012), Niagara Falls, Ontario, Canada, August 27-29, 2012 UR - http://dx.doi.org/10.1016/j.procs.2012.08.001 ER - TY - JOUR T1 - Data Science for Simulating the Era of Electric Vehicles JF - KI - Künstliche Intelligenz Y1 - 2012 A1 - Davy Janssens A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - S Rinzivillo ER - TY - CONF T1 - Individual Mobility Profiles: Methods and Application on Vehicle Sharing T2 - Twentieth Italian Symposium on Advanced Database Systems, {SEBD} 2012, Venice, Italy, June 24-27, 2012, Proceedings Y1 - 2012 A1 - Roberto Trasarti A1 - Fabio Pinelli A1 - Mirco Nanni A1 - Fosca Giannotti JF - Twentieth Italian Symposium on Advanced Database Systems, {SEBD} 2012, Venice, Italy, June 24-27, 2012, Proceedings UR - http://sebd2012.dei.unipd.it/documents/188475/32d00b8a-8ead-4d97-923f-bd2f2cf6ddcb ER - TY - CONF T1 - Mining mobility user profiles for car pooling T2 - KDD Y1 - 2011 A1 - Roberto Trasarti A1 - Fabio Pinelli A1 - Mirco Nanni A1 - Fosca Giannotti JF - KDD ER - TY - JOUR T1 - A Query Language for Mobility Data Mining JF - IJDWM Y1 - 2011 A1 - Roberto Trasarti A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Chiara Renso VL - 7 ER - TY - CONF T1 - Traffic Jams Detection Using Flock Mining T2 - ECML/PKDD (3) Y1 - 2011 A1 - Rebecca Ong A1 - Fabio Pinelli A1 - Roberto Trasarti A1 - Mirco Nanni A1 - Chiara Renso A1 - S Rinzivillo A1 - Fosca Giannotti JF - ECML/PKDD (3) ER - TY - JOUR T1 - Unveiling the complexity of human mobility by querying and mining massive trajectory data JF - VLDB J. Y1 - 2011 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Fabio Pinelli A1 - Chiara Renso A1 - S Rinzivillo A1 - Roberto Trasarti VL - 20 ER - TY - CONF T1 - Advanced knowledge discovery on movement data with the GeoPKDD system T2 - EDBT Y1 - 2010 A1 - Mirco Nanni A1 - Roberto Trasarti A1 - Chiara Renso A1 - Fosca Giannotti A1 - Dino Pedreschi JF - EDBT ER - TY - CONF T1 - Advanced knowledge discovery on movement data with the GeoPKDD system T2 - EDBT Y1 - 2010 A1 - Mirco Nanni A1 - Roberto Trasarti A1 - Chiara Renso A1 - Fosca Giannotti A1 - Dino Pedreschi JF - EDBT ER - TY - CONF T1 - Exploring Real Mobility Data with M-Atlas T2 - ECML/PKDD (3) Y1 - 2010 A1 - Roberto Trasarti A1 - S Rinzivillo A1 - Fabio Pinelli A1 - Mirco Nanni A1 - Anna Monreale A1 - Chiara Renso A1 - Dino Pedreschi A1 - Fosca Giannotti AB - Research on moving-object data analysis has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing location aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks. These have made available massive repositories of spatio-temporal data recording human mobile activities, that call for suitable analytical methods, capable of enabling the development of innovative, location-aware applications. JF - ECML/PKDD (3) ER - TY - CONF T1 - Mobility data mining: discovering movement patterns from trajectory data T2 - Computational Transportation Science Y1 - 2010 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Fabio Pinelli A1 - Chiara Renso A1 - S Rinzivillo A1 - Roberto Trasarti JF - Computational Transportation Science ER - TY - CONF T1 - Querying and mining trajectories with gaps: a multi-path reconstruction approach (Extended Abstract) T2 - SEBD Y1 - 2010 A1 - Mirco Nanni A1 - Roberto Trasarti JF - SEBD ER - TY - CHAP T1 - Spatio-temporal clustering T2 - Data Mining and Knowledge Discovery Handbook Y1 - 2010 A1 - Slava Kisilevich A1 - Florian Mansmann A1 - Mirco Nanni A1 - S Rinzivillo JF - Data Mining and Knowledge Discovery Handbook ER - TY - CONF T1 - GeoPKDD – Geographic Privacy-aware Knowledge Discovery T2 - The European Future Technologies Conference (FET 2009) Y1 - 2009 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Chiara Renso A1 - S Rinzivillo A1 - Roberto Trasarti JF - The European Future Technologies Conference (FET 2009) ER - TY - CONF T1 - K-BestMatch Reconstruction and Comparison of Trajectory Data T2 - ICDM Workshops Y1 - 2009 A1 - Mirco Nanni A1 - Roberto Trasarti JF - ICDM Workshops ER - TY - CONF T1 - K-BestMatch Reconstruction and Comparison of Trajectory Data T2 - ICDM Workshops Y1 - 2009 A1 - Mirco Nanni A1 - Roberto Trasarti JF - ICDM Workshops ER - TY - CONF T1 - Mining Mobility Behavior from Trajectory Data T2 - CSE (4) Y1 - 2009 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Chiara Renso A1 - Roberto Trasarti JF - CSE (4) ER - TY - CONF T1 - Temporal mining for interactive workflow data analysis T2 - KDD Y1 - 2009 A1 - Michele Berlingerio A1 - Fabio Pinelli A1 - Mirco Nanni A1 - Fosca Giannotti JF - KDD ER - TY - CONF T1 - Trajectory pattern analysis for urban traffic T2 - Second International Workshop on Computational Transportation Science Y1 - 2009 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Fabio Pinelli JF - Second International Workshop on Computational Transportation Science PB - ACM CY - SEATTLE, USA ER - TY - CONF T1 - A Visual Analytics Toolkit for Cluster-Based Classification of Mobility Data T2 - SSTD Y1 - 2009 A1 - Gennady Andrienko A1 - Natalia Andrienko A1 - S Rinzivillo A1 - Mirco Nanni A1 - Dino Pedreschi JF - SSTD ER - TY - CONF T1 - Visual Cluster Analysis of Large Collections of Trajectories T2 - IEEE Visual Analytics Science and Tecnology (VAST 2009) Y1 - 2009 A1 - Gennady Andrienko A1 - Natalia Andrienko A1 - S Rinzivillo A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Fosca Giannotti JF - IEEE Visual Analytics Science and Tecnology (VAST 2009) PB - IEEE Computer Society Press ER - TY - CHAP T1 - Spatiotemporal Data Mining T2 - Mobility, Data Mining and Privacy Y1 - 2008 A1 - Mirco Nanni A1 - Bart Kuijpers A1 - Christine Körner A1 - Michael May A1 - Dino Pedreschi JF - Mobility, Data Mining and Privacy ER - TY - CONF T1 - Temporal analysis of process logs: a case study T2 - SEBD Y1 - 2008 A1 - Michele Berlingerio A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Fabio Pinelli JF - SEBD ER - TY - JOUR T1 - Visually driven analysis of movement data by progressive clustering JF - Information Visualization Y1 - 2008 A1 - S Rinzivillo A1 - Dino Pedreschi A1 - Mirco Nanni A1 - Fosca Giannotti A1 - Natalia Andrienko A1 - Gennady Andrienko PB - Palgrave Macmillan Ltd VL - 7 ER - TY - CONF T1 - Trajectory pattern mining T2 - KDD Y1 - 2007 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Fabio Pinelli A1 - Dino Pedreschi JF - KDD ER - TY - CONF T1 - Efficient Mining of Temporally Annotated Sequences T2 - SDM Y1 - 2006 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi JF - SDM ER - TY - CONF T1 - Mining sequences with temporal annotations T2 - SAC Y1 - 2006 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Fabio Pinelli JF - SAC ER - TY - JOUR T1 - Time-focused clustering of trajectories of moving objects JF - J. Intell. Inf. Syst. Y1 - 2006 A1 - Mirco Nanni A1 - Dino Pedreschi VL - 27 ER - TY - CONF T1 - Deductive and Inductive Reasoning on Spatio-Temporal Data T2 - INAP/WLP Y1 - 2004 A1 - Mirco Nanni A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Franco Turini JF - INAP/WLP ER - TY - CONF T1 - Deductive and Inductive Reasoning on Trajectories T2 - SEBD Y1 - 2004 A1 - Mirco Nanni A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Franco Turini JF - SEBD ER - TY - CONF T1 - Discovery of ads web hosts through traffic data analysis T2 - DMKD Y1 - 2004 A1 - V. Bacarella A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi JF - DMKD ER - TY - JOUR T1 - \newblock{A Declarative Framework for Reasoning on Spatio-temporal Data} Y1 - 2004 A1 - Mirco Nanni A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Franco Turini N1 - \newblock{Book chapter in Spatio-temporal databases, flexible querying and reasoning, R. de Caluwe, G. de Trè, G. Bordogna editors, Physica Verlag }. ER - TY - CONF T1 - WebCat: Automatic Categorization of Web Search Results T2 - SEBD Y1 - 2003 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - F. Samaritani JF - SEBD ER - TY - CONF T1 - Data Mining for Intelligent Web Caching T2 - ITCC Y1 - 2001 A1 - Francesco Bonchi A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Chiara Renso A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Salvatore Ruggieri JF - ITCC ER - TY - CONF T1 - Data Mining for Intelligent Web Caching T2 - ITCC Y1 - 2001 A1 - Francesco Bonchi A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Chiara Renso A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Salvatore Ruggieri JF - ITCC ER - TY - JOUR T1 - Nondeterministic, Nonmonotonic Logic Databases JF - IEEE Trans. Knowl. Data Eng. Y1 - 2001 A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Mirco Nanni A1 - Dino Pedreschi VL - 13 ER - TY - JOUR T1 - Web log data warehousing and mining for intelligent web caching JF - Data Knowl. Eng. Y1 - 2001 A1 - Francesco Bonchi A1 - Fosca Giannotti A1 - Cristian Gozzi A1 - Giuseppe Manco A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Chiara Renso A1 - Salvatore Ruggieri VL - 39 ER - TY - JOUR T1 - Web log data warehousing and mining for intelligent web caching JF - Data Knowl. Eng. Y1 - 2001 A1 - Francesco Bonchi A1 - Fosca Giannotti A1 - Cristian Gozzi A1 - Giuseppe Manco A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Chiara Renso A1 - Salvatore Ruggieri VL - 39 ER - TY - JOUR T1 - Web Log Data Warehousing and Mining for Intelligent Web Caching JF - Data and Knowledge Engineering Y1 - 2001 A1 - Francesco Bonchi A1 - Fosca Giannotti A1 - Cristian Gozzi A1 - Giuseppe Manco A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Chiara Renso A1 - Salvatore Ruggieri N1 - 39:165, November . ER - TY - JOUR T1 - Foundations of distributed interaction systems JF - Ann. Math. Artif. Intell. Y1 - 2000 A1 - Marat Fayzullin A1 - Mirco Nanni A1 - Dino Pedreschi A1 - V. S. Subrahmanian VL - 28 ER - TY - CONF T1 - Logic-Based Knowledge Discovery in Databases T2 - EJC Y1 - 2000 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi JF - EJC ER - TY - CONF T1 - Integration of Deduction and Induction for Mining Supermarket Sales Data T2 - SEBD Y1 - 1999 A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Franco Turini JF - SEBD ER - TY - CONF T1 - On the Effective Semantics of Nondeterministic, Nonmonotonic, Temporal Logic Databases T2 - CSL Y1 - 1998 A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Mirco Nanni A1 - Dino Pedreschi JF - CSL ER - TY - CONF T1 - Query Answering in Nondeterministic, Nonmonotonic Logic Databases T2 - FQAS Y1 - 1998 A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Mirco Nanni A1 - Dino Pedreschi JF - FQAS ER - TY - CONF T1 - Datalog++: A Basis for Active Object-Oriented Databases T2 - DOOD Y1 - 1997 A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Mirco Nanni A1 - Dino Pedreschi JF - DOOD ER - TY - CONF T1 - Datalog++: a Basis for Active Object.Oriented Databases T2 - SEBD Y1 - 1997 A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Mirco Nanni A1 - Dino Pedreschi JF - SEBD ER -