%0 Conference Paper %B Proceedings of the 30th International Conference on Advances in Geographic Information Systems %D 2022 %T Connected Vehicle Simulation Framework for Parking Occupancy Prediction (Demo Paper) %A Resce, Pierpaolo %A Vorwerk, Lukas %A Han, Zhiwei %A Cornacchia, Giuliano %A Alamdari, Omid Isfahani %A Mirco Nanni %A Luca Pappalardo %A Weimer, Daniel %A Liu, Yuanting %X 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. %B Proceedings of the 30th International Conference on Advances in Geographic Information Systems %I Association for Computing Machinery %C New York, NY, USA %@ 9781450395298 %G eng %U https://doi.org/10.1145/3557915.3560995 %R 10.1145/3557915.3560995 %0 Conference Paper %B Proceedings of the 30th International Conference on Advances in Geographic Information Systems %D 2022 %T How Routing Strategies Impact Urban Emissions %A Cornacchia, Giuliano %A Böhm, Matteo %A Giovanni Mauro %A Mirco Nanni %A Dino Pedreschi %A Luca Pappalardo %X 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. %B Proceedings of the 30th International Conference on Advances in Geographic Information Systems %I Association for Computing Machinery %C New York, NY, USA %@ 9781450395298 %G eng %U https://doi.org/10.1145/3557915.3560977 %R 10.1145/3557915.3560977 %0 Journal Article %D 2021 %T Give more data, awareness and control to individual citizens, and they will help COVID-19 containment %A Mirco Nanni %A Andrienko, Gennady %A Barabasi, Albert-Laszlo %A Boldrini, Chiara %A Bonchi, Francesco %A Cattuto, Ciro %A Chiaromonte, Francesca %A Comandé, Giovanni %A Conti, Marco %A Coté, Mark %A Dignum, Frank %A Dignum, Virginia %A Domingo-Ferrer, Josep %A Ferragina, Paolo %A Fosca Giannotti %A Riccardo Guidotti %A Helbing, Dirk %A Kaski, Kimmo %A Kertész, János %A Lehmann, Sune %A Lepri, Bruno %A Lukowicz, Paul %A Matwin, Stan %A Jiménez, David Megías %A Anna Monreale %A Morik, Katharina %A Oliver, Nuria %A Passarella, Andrea %A Passerini, Andrea %A Dino Pedreschi %A Pentland, Alex %A Pianesi, Fabio %A Francesca Pratesi %A S Rinzivillo %A Salvatore Ruggieri %A Siebes, Arno %A Torra, Vicenc %A Roberto Trasarti %A Hoven, Jeroen van den %A Vespignani, Alessandro %X 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. %8 2021/02/02 %@ 1572-8439 %G eng %U https://link.springer.com/article/10.1007/s10676-020-09572-w %! Ethics and Information Technology %R https://doi.org/10.1007/s10676-020-09572-w %0 Generic %D 2020 %T Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown %A Pietro Bonato %A Paolo Cintia %A Francesco Fabbri %A Daniele Fadda %A Fosca Giannotti %A Pier Luigi Lopalco %A Sara Mazzilli %A Mirco Nanni %A Luca Pappalardo %A Dino Pedreschi %A Francesco Penone %A S Rinzivillo %A Giulio Rossetti %A Marcello Savarese %A Lara Tavoschi %X 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? %G eng %U https://arxiv.org/abs/2004.11278 %R https://dx.doi.org/10.32079/ISTI-TR-2020/005 %0 Conference Paper %B International Workshop on Complex Networks %D 2018 %T Discovering Mobility Functional Areas: A Mobility Data Analysis Approach %A Lorenzo Gabrielli %A Daniele Fadda %A Giulio Rossetti %A Mirco Nanni %A Piccinini, Leonardo %A Dino Pedreschi %A Fosca Giannotti %A Patrizia Lattarulo %X 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. %B International Workshop on Complex Networks %I Springer %G eng %U https://link.springer.com/chapter/10.1007/978-3-319-73198-8_27 %R 10.1007/978-3-319-73198-8_27 %0 Book Section %B A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years %D 2018 %T How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science %A Amato, G. %A Candela, L. %A Castelli, D. %A Esuli, A. %A Falchi, F. %A Gennaro, C. %A Fosca Giannotti %A Anna Monreale %A Mirco Nanni %A Pagano, P. %A Luca Pappalardo %A Dino Pedreschi %A Francesca Pratesi %A Rabitti, F. %A S Rinzivillo %A Giulio Rossetti %A Salvatore Ruggieri %A Sebastiani, F. %A Tesconi, M. %E Flesca, Sergio %E Greco, Sergio %E Masciari, Elio %E Saccà, Domenico %X 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. %B A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years %I Springer International Publishing %C Cham %P 287 - 306 %@ 978-3-319-61893-7 %G eng %U https://link.springer.com/chapter/10.1007%2F978-3-319-61893-7_17 %R https://doi.org/10.1007/978-3-319-61893-7_17 %0 Conference Paper %B Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining %D 2017 %T Clustering Individual Transactional Data for Masses of Users %A Riccardo Guidotti %A Anna Monreale %A Mirco Nanni %A Fosca Giannotti %A Dino Pedreschi %X 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 %B Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining %I ACM %G eng %R 10.1145/3097983.3098034 %0 Journal Article %J Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach %D 2017 %T ICON Loop Carpooling Show Case %A Mirco Nanni %A Lars Kotthoff %A Riccardo Guidotti %A Barry O'Sullivan %A Dino Pedreschi %X 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. %B Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach %V 10101 %P 310 %G eng %U https://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf#page=314 %0 Journal Article %J IEEE Intelligent Systems %D 2017 %T The Inductive Constraint Programming Loop %A Bessiere, Christian %A De Raedt, Luc %A Tias Guns %A Lars Kotthoff %A Mirco Nanni %A Siegfried Nijssen %A Barry O'Sullivan %A Paparrizou, Anastasia %A Dino Pedreschi %A Simonis, Helmut %X 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. %B IEEE Intelligent Systems %G eng %R 10.1109/MIS.2017.265115706 %0 Journal Article %J Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach %D 2017 %T The Inductive Constraint Programming Loop %A Mirco Nanni %A Siegfried Nijssen %A Barry O'Sullivan %A Paparrizou, Anastasia %A Dino Pedreschi %A Simonis, Helmut %X 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. %B Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach %V 10101 %P 303 %G eng %U https://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf#page=307 %0 Conference Paper %B 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 %D 2017 %T Movement Behaviour Recognition for Water Activities %A Mirco Nanni %A Roberto Trasarti %A Fosca Giannotti %B 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 %G eng %U https://doi.org/10.1007/978-3-319-71970-2_7 %R 10.1007/978-3-319-71970-2_7 %0 Journal Article %J Information Systems %D 2017 %T Never drive alone: Boosting carpooling with network analysis %A Riccardo Guidotti %A Mirco Nanni %A S Rinzivillo %A Dino Pedreschi %A Fosca Giannotti %X 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. %B Information Systems %V 64 %P 237–257 %G eng %R 10.1016/j.is.2016.03.006 %0 Journal Article %J I. J. Data Science and Analytics %D 2017 %T Scalable and flexible clustering solutions for mobile phone-based population indicators %A Alessandro Lulli %A Lorenzo Gabrielli %A Patrizio Dazzi %A Matteo Dell'Amico %A Pietro Michiardi %A Mirco Nanni %A Laura Ricci %B I. J. Data Science and Analytics %V 4 %P 285–299 %G eng %U https://doi.org/10.1007/s41060-017-0065-y %R 10.1007/s41060-017-0065-y %0 Conference Paper %B 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017) %D 2017 %T There's A Path For Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas %A Riccardo Guidotti %A Roberto Trasarti %A Mirco Nanni %A Fosca Giannotti %A Dino Pedreschi %B 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017) %I IEEE %C Tokyo %G eng %0 Conference Paper %B SEBD - Italian Symposium on Advanced Database Systems %D 2016 %T Big Data and Public Administration: a case study for Tuscany Airports %A Barbara Furletti %A Daniele Fadda %A Leonardo Piccini %A Mirco Nanni %A Patrizia Lattarulo %X 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. %B SEBD - Italian Symposium on Advanced Database Systems %I Matematicamente.it %C Ugento, Lecce (Italy) %8 06/2016 %@ 9788896354889 %G eng %U http://sebd2016.unisalento.it/grid/SEBD2016-proceedings.pdf %0 Journal Article %J Journal ACM Transactions on Intelligent Systems and Technology (TIST) %D 2016 %T Driving Profiles Computation and Monitoring for Car Insurance CRM %A Mirco Nanni %A Roberto Trasarti %A Anna Monreale %A Valerio Grossi %A Dino Pedreschi %X 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. %B Journal ACM Transactions on Intelligent Systems and Technology (TIST) %V 8 %P 14:1–14:26 %G eng %U http://doi.acm.org/10.1145/2912148 %R 10.1145/2912148 %0 Book Section %B Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach %D 2016 %T Partition-Based Clustering Using Constraint Optimization %A Valerio Grossi %A Tias Guns %A Anna Monreale %A Mirco Nanni %A Siegfried Nijssen %X 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. %B Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach %I Springer International Publishing %P 282–299 %G eng %U http://dx.doi.org/10.1007/978-3-319-50137-6_11 %R 10.1007/978-3-319-50137-6_11 %0 Conference Paper %B Software Engineering and Formal Methods - {SEFM} 2015 Collocated Workshops: ATSE, HOFM, MoKMaSD, and VERY*SCART, York, UK, September 7-8, 2015, Revised Selected Papers %D 2015 %T Clustering Formulation Using Constraint Optimization %A Valerio Grossi %A Anna Monreale %A Mirco Nanni %A Dino Pedreschi %A Franco Turini %X 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. %B Software Engineering and Formal Methods - {SEFM} 2015 Collocated Workshops: ATSE, HOFM, MoKMaSD, and VERY*SCART, York, UK, September 7-8, 2015, Revised Selected Papers %I Springer Berlin Heidelberg %G eng %U http://dx.doi.org/10.1007/978-3-662-49224-6_9 %R 10.1007/978-3-662-49224-6_9 %0 Conference Paper %B Principles and Practice of Constraint Programming %D 2015 %T Find Your Way Back: Mobility Profile Mining with Constraints %A Lars Kotthoff %A Mirco Nanni %A Riccardo Guidotti %A Barry O'Sullivan %X 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. %B Principles and Practice of Constraint Programming %I Springer International Publishing %C Cork %G eng %0 Conference Paper %B Proceedings of the 23rd {SIGSPATIAL} International Conference on Advances in Geographic Information Systems, Bellevue, WA, USA, November 3-6, 2015 %D 2015 %T {TOSCA:} two-steps clustering algorithm for personal locations detection %A Riccardo Guidotti %A Roberto Trasarti %A Mirco Nanni %B Proceedings of the 23rd {SIGSPATIAL} International Conference on Advances in Geographic Information Systems, Bellevue, WA, USA, November 3-6, 2015 %G eng %U http://doi.acm.org/10.1145/2820783.2820818 %R 10.1145/2820783.2820818 %0 Conference Paper %B Proceedings of the 4th {ACM} {SIGSPATIAL} International Workshop on Mobile Geographic Information Systems, MobiGIS 2015, Bellevue, WA, USA, November 3-6, 2015 %D 2015 %T Towards user-centric data management: individual mobility analytics for collective services %A Riccardo Guidotti %A Roberto Trasarti %A Mirco Nanni %A Fosca Giannotti %B Proceedings of the 4th {ACM} {SIGSPATIAL} International Workshop on Mobile Geographic Information Systems, MobiGIS 2015, Bellevue, WA, USA, November 3-6, 2015 %G eng %U http://doi.acm.org/10.1145/2834126.2834132 %R 10.1145/2834126.2834132 %0 Book Section %B Software Engineering and Formal Methods %D 2015 %T Use of Mobile Phone Data to Estimate Visitors Mobility Flows %A Lorenzo Gabrielli %A Barbara Furletti %A Fosca Giannotti %A Mirco Nanni %A S Rinzivillo %X 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. %B Software Engineering and Formal Methods %I Springer International Publishing %V 8938 %P 214-226 %G eng %U http://link.springer.com/chapter/10.1007%2F978-3-319-15201-1_14 %R 10.1007/978-3-319-15201-1_14 %0 Conference Paper %B EDBT/ICDT 2014 Workshops - Mining Urban Data (MUD) %D 2014 %T Big data analytics for smart mobility: a case study %A Barbara Furletti %A Roberto Trasarti %A Lorenzo Gabrielli %A Mirco Nanni %A Dino Pedreschi %B EDBT/ICDT 2014 Workshops - Mining Urban Data (MUD) %C Athens, Greece %8 03/2014 %U http://ceur-ws.org/Vol-1133/paper-57.pdf %M ISSN - 1613-0073 %0 Journal Article %J Telecommunications Policy %D 2014 %T Discovering urban and country dynamics from mobile phone data with spatial correlation patterns %A Roberto Trasarti %A Ana-Maria Olteanu-Raimond %A Mirco Nanni %A Thomas Couronné %A Barbara Furletti %A Fosca Giannotti %A Zbigniew Smoreda %A Cezary Ziemlicki %K Urban dynamics %X 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. %B Telecommunications Policy %P - %U http://www.sciencedirect.com/science/article/pii/S0308596113002012 %R http://dx.doi.org/10.1016/j.telpol.2013.12.002 %0 Book Section %B Data Science and Simulation in Transportation Research %D 2014 %T Mobility Profiling %A Mirco Nanni %A Roberto Trasarti %A Paolo Cintia %A Barbara Furletti %A Chiara Renso %A Lorenzo Gabrielli %A S Rinzivillo %A Fosca Giannotti %X 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. %B Data Science and Simulation in Transportation Research %I IGI Global %P 1-29 %& 1 %R 10.4018/978-1-4666-4920-0.ch001 %0 Conference Paper %B International Conference on Data Science and Advanced Analytics, {DSAA} 2014, Shanghai, China, October 30 - November 1, 2014 %D 2014 %T The purpose of motion: Learning activities from Individual Mobility Networks %A S Rinzivillo %A Lorenzo Gabrielli %A Mirco Nanni %A Luca Pappalardo %A Dino Pedreschi %A Fosca Giannotti %B International Conference on Data Science and Advanced Analytics, {DSAA} 2014, Shanghai, China, October 30 - November 1, 2014 %G eng %U http://dx.doi.org/10.1109/DSAA.2014.7058090 %R 10.1109/DSAA.2014.7058090 %0 Conference Paper %B 47th SIS Scientific Meeting of the Italian Statistica Society %D 2014 %T Use of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach %A Barbara Furletti %A Lorenzo Gabrielli %A Fosca Giannotti %A Letizia Milli %A Mirco Nanni %A Dino Pedreschi %? Roberta Vivio %? Giuseppe Garofalo %X 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) %B 47th SIS Scientific Meeting of the Italian Statistica Society %C Cagliari %8 06/2014 %@ 978-88-8467-874-4 %U http://www.sis2014.it/proceedings/allpapers/3026.pdf %0 Conference Paper %B Proceedings of MoKMaSD %D 2014 %T Use of mobile phone data to estimate visitors mobility flows %A Lorenzo Gabrielli %A Barbara Furletti %A Fosca Giannotti %A Mirco Nanni %A S Rinzivillo %X 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 %B Proceedings of MoKMaSD %U http://www.di.unipi.it/mokmasd/symposium-2014/preproceedings/GabrielliEtAl-mokmasd2014.pdf %0 Conference Paper %B In D4D Challenge @ 3rd Conf. on the Analysis of Mobile Phone datasets (NetMob 2013) %D 2013 %T MP4-A Project: Mobility Planning For Africa %A Mirco Nanni %A Roberto Trasarti %A Barbara Furletti %A Lorenzo Gabrielli %A Peter Van Der Mede %A Joost De Bruijn %A Erik de Romph %A Gerard Bruil %X 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. %B In D4D Challenge @ 3rd Conf. on the Analysis of Mobile Phone datasets (NetMob 2013) %C Cambridge, USA %G eng %U http://perso.uclouvain.be/vincent.blondel/netmob/2013/D4D-book.pdf %0 Journal Article %J Spatio-Temporal Databases: Flexible Querying and Reasoning %D 2013 %T Spatio-Temporal Data %A Mirco Nanni %A Alessandra Raffaetà %A Chiara Renso %A Franco Turini %B Spatio-Temporal Databases: Flexible Querying and Reasoning %P 75 %G eng %0 Conference Paper %B Mining Complex Patterns Workshop, ECML PKDD 2013 %D 2013 %T A Study on Parameter Estimation for a Mining Flock Algorithm %A Rebecca Ong %A Mirco Nanni %A Chiara Renso %A Monica Wachowicz %A Dino Pedreschi %B Mining Complex Patterns Workshop, ECML PKDD 2013 %0 Conference Paper %B Citizen in Sensor Networks - Second International Workshop, CitiSens 2013, Barcelona, Spain, September 19, 2013, Revised Selected Papers %D 2013 %T Transportation Planning Based on {GSM} Traces: {A} Case Study on Ivory Coast %A Mirco Nanni %A Roberto Trasarti %A Barbara Furletti %A Lorenzo Gabrielli %A Peter Van Der Mede %A Joost De Bruijn %A Erik de Romph %A Gerard Bruil %B Citizen in Sensor Networks - Second International Workshop, CitiSens 2013, Barcelona, Spain, September 19, 2013, Revised Selected Papers %G eng %U http://dx.doi.org/10.1007/978-3-319-04178-0_2 %R 10.1007/978-3-319-04178-0_2 %0 Conference Paper %B 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 %D 2012 %T An Agent-Based Model to Evaluate Carpooling at Large Manufacturing Plants %A Tom Bellemans %A Sebastian Bothe %A Sungjin Cho %A Fosca Giannotti %A Davy Janssens %A Luk Knapen %A Christine Körner %A Michael May %A Mirco Nanni %A Dino Pedreschi %A Hendrik Stange %A Roberto Trasarti %A Ansar-Ul-Haque Yasar %A Geert Wets %B 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 %G eng %U http://dx.doi.org/10.1016/j.procs.2012.08.001 %R 10.1016/j.procs.2012.08.001 %0 Journal Article %J KI - Künstliche Intelligenz %D 2012 %T Data Science for Simulating the Era of Electric Vehicles %A Davy Janssens %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %A S Rinzivillo %B KI - Künstliche Intelligenz %R 10.1007/s13218-012-0183-6 %0 Conference Paper %B Twentieth Italian Symposium on Advanced Database Systems, {SEBD} 2012, Venice, Italy, June 24-27, 2012, Proceedings %D 2012 %T Individual Mobility Profiles: Methods and Application on Vehicle Sharing %A Roberto Trasarti %A Fabio Pinelli %A Mirco Nanni %A Fosca Giannotti %B Twentieth Italian Symposium on Advanced Database Systems, {SEBD} 2012, Venice, Italy, June 24-27, 2012, Proceedings %G eng %U http://sebd2012.dei.unipd.it/documents/188475/32d00b8a-8ead-4d97-923f-bd2f2cf6ddcb %0 Conference Paper %B KDD %D 2011 %T Mining mobility user profiles for car pooling %A Roberto Trasarti %A Fabio Pinelli %A Mirco Nanni %A Fosca Giannotti %B KDD %P 1190-1198 %0 Journal Article %J IJDWM %D 2011 %T A Query Language for Mobility Data Mining %A Roberto Trasarti %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %A Chiara Renso %B IJDWM %V 7 %P 24-45 %0 Conference Paper %B ECML/PKDD (3) %D 2011 %T Traffic Jams Detection Using Flock Mining %A Rebecca Ong %A Fabio Pinelli %A Roberto Trasarti %A Mirco Nanni %A Chiara Renso %A S Rinzivillo %A Fosca Giannotti %B ECML/PKDD (3) %P 650-653 %0 Journal Article %J VLDB J. %D 2011 %T Unveiling the complexity of human mobility by querying and mining massive trajectory data %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %A Fabio Pinelli %A Chiara Renso %A S Rinzivillo %A Roberto Trasarti %B VLDB J. %V 20 %P 695-719 %0 Conference Paper %B EDBT %D 2010 %T Advanced knowledge discovery on movement data with the GeoPKDD system %A Mirco Nanni %A Roberto Trasarti %A Chiara Renso %A Fosca Giannotti %A Dino Pedreschi %B EDBT %P 693-696 %0 Conference Paper %B EDBT %D 2010 %T Advanced knowledge discovery on movement data with the GeoPKDD system %A Mirco Nanni %A Roberto Trasarti %A Chiara Renso %A Fosca Giannotti %A Dino Pedreschi %B EDBT %P 693-696 %0 Conference Paper %B ECML/PKDD (3) %D 2010 %T Exploring Real Mobility Data with M-Atlas %A Roberto Trasarti %A S Rinzivillo %A Fabio Pinelli %A Mirco Nanni %A Anna Monreale %A Chiara Renso %A Dino Pedreschi %A Fosca Giannotti %X 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. %B ECML/PKDD (3) %P 624-627 %R 10.1007/978-3-642-15939-8_48 %0 Conference Paper %B Computational Transportation Science %D 2010 %T Mobility data mining: discovering movement patterns from trajectory data %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %A Fabio Pinelli %A Chiara Renso %A S Rinzivillo %A Roberto Trasarti %B Computational Transportation Science %P 7-10 %0 Conference Paper %B SEBD %D 2010 %T Querying and mining trajectories with gaps: a multi-path reconstruction approach (Extended Abstract) %A Mirco Nanni %A Roberto Trasarti %B SEBD %P 126-133 %0 Book Section %B Data Mining and Knowledge Discovery Handbook %D 2010 %T Spatio-temporal clustering %A Slava Kisilevich %A Florian Mansmann %A Mirco Nanni %A S Rinzivillo %B Data Mining and Knowledge Discovery Handbook %P 855-874 %0 Conference Paper %B The European Future Technologies Conference (FET 2009) %D 2009 %T GeoPKDD – Geographic Privacy-aware Knowledge Discovery %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %A Chiara Renso %A S Rinzivillo %A Roberto Trasarti %B The European Future Technologies Conference (FET 2009) %0 Conference Paper %B ICDM Workshops %D 2009 %T K-BestMatch Reconstruction and Comparison of Trajectory Data %A Mirco Nanni %A Roberto Trasarti %B ICDM Workshops %P 610-615 %0 Conference Paper %B ICDM Workshops %D 2009 %T K-BestMatch Reconstruction and Comparison of Trajectory Data %A Mirco Nanni %A Roberto Trasarti %B ICDM Workshops %P 610-615 %0 Conference Paper %B CSE (4) %D 2009 %T Mining Mobility Behavior from Trajectory Data %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %A Chiara Renso %A Roberto Trasarti %B CSE (4) %P 948-951 %0 Conference Paper %B KDD %D 2009 %T Temporal mining for interactive workflow data analysis %A Michele Berlingerio %A Fabio Pinelli %A Mirco Nanni %A Fosca Giannotti %B KDD %P 109-118 %0 Conference Paper %B Second International Workshop on Computational Transportation Science %D 2009 %T Trajectory pattern analysis for urban traffic %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %A Fabio Pinelli %B Second International Workshop on Computational Transportation Science %I ACM %C SEATTLE, USA %P 43-47 %8 11/2009 %0 Conference Paper %B SSTD %D 2009 %T A Visual Analytics Toolkit for Cluster-Based Classification of Mobility Data %A Gennady Andrienko %A Natalia Andrienko %A S Rinzivillo %A Mirco Nanni %A Dino Pedreschi %B SSTD %P 432-435 %0 Conference Paper %B IEEE Visual Analytics Science and Tecnology (VAST 2009) %D 2009 %T Visual Cluster Analysis of Large Collections of Trajectories %A Gennady Andrienko %A Natalia Andrienko %A S Rinzivillo %A Mirco Nanni %A Dino Pedreschi %A Fosca Giannotti %B IEEE Visual Analytics Science and Tecnology (VAST 2009) %I IEEE Computer Society Press %0 Book Section %B Mobility, Data Mining and Privacy %D 2008 %T Spatiotemporal Data Mining %A Mirco Nanni %A Bart Kuijpers %A Christine Körner %A Michael May %A Dino Pedreschi %B Mobility, Data Mining and Privacy %P 267-296 %0 Conference Paper %B SEBD %D 2008 %T Temporal analysis of process logs: a case study %A Michele Berlingerio %A Fosca Giannotti %A Mirco Nanni %A Fabio Pinelli %B SEBD %P 430-437 %G eng %0 Journal Article %J Information Visualization %D 2008 %T Visually driven analysis of movement data by progressive clustering %A S Rinzivillo %A Dino Pedreschi %A Mirco Nanni %A Fosca Giannotti %A Natalia Andrienko %A Gennady Andrienko %B Information Visualization %I Palgrave Macmillan Ltd %V 7 %P 225-239 %0 Conference Paper %B KDD %D 2007 %T Trajectory pattern mining %A Fosca Giannotti %A Mirco Nanni %A Fabio Pinelli %A Dino Pedreschi %B KDD %P 330-339 %G eng %0 Conference Paper %B SDM %D 2006 %T Efficient Mining of Temporally Annotated Sequences %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %B SDM %G eng %0 Conference Paper %B SAC %D 2006 %T Mining sequences with temporal annotations %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %A Fabio Pinelli %B SAC %P 593-597 %G eng %0 Journal Article %J J. Intell. Inf. Syst. %D 2006 %T Time-focused clustering of trajectories of moving objects %A Mirco Nanni %A Dino Pedreschi %B J. Intell. Inf. Syst. %V 27 %P 267-289 %G eng %0 Conference Paper %B INAP/WLP %D 2004 %T Deductive and Inductive Reasoning on Spatio-Temporal Data %A Mirco Nanni %A Alessandra Raffaetà %A Chiara Renso %A Franco Turini %B INAP/WLP %P 98-115 %G eng %0 Conference Paper %B SEBD %D 2004 %T Deductive and Inductive Reasoning on Trajectories %A Mirco Nanni %A Alessandra Raffaetà %A Chiara Renso %A Franco Turini %B SEBD %P 98-105 %G eng %0 Conference Paper %B DMKD %D 2004 %T Discovery of ads web hosts through traffic data analysis %A V. Bacarella %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %B DMKD %P 76-81 %G eng %0 Journal Article %D 2004 %T \newblock{A Declarative Framework for Reasoning on Spatio-temporal Data} %A Mirco Nanni %A Alessandra Raffaetà %A Chiara Renso %A Franco Turini %G eng %0 Conference Paper %B SEBD %D 2003 %T WebCat: Automatic Categorization of Web Search Results %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %A F. Samaritani %B SEBD %P 507-518 %G eng %0 Conference Paper %B ITCC %D 2001 %T Data Mining for Intelligent Web Caching %A Francesco Bonchi %A Fosca Giannotti %A Giuseppe Manco %A Chiara Renso %A Mirco Nanni %A Dino Pedreschi %A Salvatore Ruggieri %B ITCC %P 599-603 %G eng %0 Conference Paper %B ITCC %D 2001 %T Data Mining for Intelligent Web Caching %A Francesco Bonchi %A Fosca Giannotti %A Giuseppe Manco %A Chiara Renso %A Mirco Nanni %A Dino Pedreschi %A Salvatore Ruggieri %B ITCC %P 599-603 %G eng %0 Journal Article %J IEEE Trans. Knowl. Data Eng. %D 2001 %T Nondeterministic, Nonmonotonic Logic Databases %A Fosca Giannotti %A Giuseppe Manco %A Mirco Nanni %A Dino Pedreschi %B IEEE Trans. Knowl. Data Eng. %V 13 %P 813-823 %G eng %0 Journal Article %J Data Knowl. Eng. %D 2001 %T Web log data warehousing and mining for intelligent web caching %A Francesco Bonchi %A Fosca Giannotti %A Cristian Gozzi %A Giuseppe Manco %A Mirco Nanni %A Dino Pedreschi %A Chiara Renso %A Salvatore Ruggieri %B Data Knowl. Eng. %V 39 %P 165-189 %G eng %0 Journal Article %J Data Knowl. Eng. %D 2001 %T Web log data warehousing and mining for intelligent web caching %A Francesco Bonchi %A Fosca Giannotti %A Cristian Gozzi %A Giuseppe Manco %A Mirco Nanni %A Dino Pedreschi %A Chiara Renso %A Salvatore Ruggieri %B Data Knowl. Eng. %V 39 %P 165-189 %G eng %0 Journal Article %J Data and Knowledge Engineering %D 2001 %T Web Log Data Warehousing and Mining for Intelligent Web Caching %A Francesco Bonchi %A Fosca Giannotti %A Cristian Gozzi %A Giuseppe Manco %A Mirco Nanni %A Dino Pedreschi %A Chiara Renso %A Salvatore Ruggieri %B Data and Knowledge Engineering %G eng %0 Journal Article %J Ann. Math. Artif. Intell. %D 2000 %T Foundations of distributed interaction systems %A Marat Fayzullin %A Mirco Nanni %A Dino Pedreschi %A V. S. Subrahmanian %B Ann. Math. Artif. Intell. %V 28 %P 127-168 %G eng %0 Conference Paper %B EJC %D 2000 %T Logic-Based Knowledge Discovery in Databases %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %B EJC %P 279-283 %G eng %0 Conference Paper %B SEBD %D 1999 %T Integration of Deduction and Induction for Mining Supermarket Sales Data %A Fosca Giannotti %A Giuseppe Manco %A Mirco Nanni %A Dino Pedreschi %A Franco Turini %B SEBD %P 117-131 %G eng %0 Conference Paper %B CSL %D 1998 %T On the Effective Semantics of Nondeterministic, Nonmonotonic, Temporal Logic Databases %A Fosca Giannotti %A Giuseppe Manco %A Mirco Nanni %A Dino Pedreschi %B CSL %P 58-72 %G eng %0 Conference Paper %B FQAS %D 1998 %T Query Answering in Nondeterministic, Nonmonotonic Logic Databases %A Fosca Giannotti %A Giuseppe Manco %A Mirco Nanni %A Dino Pedreschi %B FQAS %P 175-187 %G eng %0 Conference Paper %B DOOD %D 1997 %T Datalog++: A Basis for Active Object-Oriented Databases %A Fosca Giannotti %A Giuseppe Manco %A Mirco Nanni %A Dino Pedreschi %B DOOD %P 283-301 %G eng %0 Conference Paper %B SEBD %D 1997 %T Datalog++: a Basis for Active Object.Oriented Databases %A Fosca Giannotti %A Giuseppe Manco %A Mirco Nanni %A Dino Pedreschi %B SEBD %P 325-340 %G eng