@conference {1493,
	title = {Connected Vehicle Simulation Framework for Parking Occupancy Prediction (Demo Paper)},
	booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems},
	year = {2022},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {This paper demonstrates a simulation framework that collects data about connected vehicles{\textquoteright} 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.},
	isbn = {9781450395298},
	doi = {10.1145/3557915.3560995},
	url = {https://doi.org/10.1145/3557915.3560995},
	author = {Resce, Pierpaolo and Vorwerk, Lukas and Han, Zhiwei and Cornacchia, Giuliano and Alamdari, Omid Isfahani and Mirco Nanni and Luca Pappalardo and Weimer, Daniel and Liu, Yuanting}
}
@conference {1492,
	title = {How Routing Strategies Impact Urban Emissions},
	booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems},
	year = {2022},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {Navigation apps use routing algorithms to suggest the best path to reach a user{\textquoteright}s desired destination. Although undoubtedly useful, navigation apps{\textquoteright} 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{\textquoteright}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{\textquoteright} 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.},
	isbn = {9781450395298},
	doi = {10.1145/3557915.3560977},
	url = {https://doi.org/10.1145/3557915.3560977},
	author = {Cornacchia, Giuliano and B{\"o}hm, Matteo and Giovanni Mauro and Mirco Nanni and Dino Pedreschi and Luca Pappalardo}
}
@article {1400,
	title = {Give more data, awareness and control to individual citizens, and they will help COVID-19 containment},
	year = {2021},
	month = {2021/02/02},
	abstract = {The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the {\textquotedblleft}phase 2{\textquotedblright} 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{\textquoteright} 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{\textquoteright} {\textquotedblleft}personal data stores{\textquotedblright}, 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{\textemdash}if and when they want and for specific aims{\textemdash}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.},
	isbn = {1572-8439},
	doi = {https://doi.org/10.1007/s10676-020-09572-w},
	url = {https://link.springer.com/article/10.1007/s10676-020-09572-w},
	author = {Mirco Nanni and Andrienko, Gennady and Barabasi, Albert-Laszlo and Boldrini, Chiara and Bonchi, Francesco and Cattuto, Ciro and Chiaromonte, Francesca and Comand{\'e}, Giovanni and Conti, Marco and Cot{\'e}, Mark and Dignum, Frank and Dignum, Virginia and Domingo-Ferrer, Josep and Ferragina, Paolo and Fosca Giannotti and Riccardo Guidotti and Helbing, Dirk and Kaski, Kimmo and Kert{\'e}sz, J{\'a}nos and Lehmann, Sune and Lepri, Bruno and Lukowicz, Paul and Matwin, Stan and Jim{\'e}nez, David Meg{\'\i}as and Anna Monreale and Morik, Katharina and Oliver, Nuria and Passarella, Andrea and Passerini, Andrea and Dino Pedreschi and Pentland, Alex and Pianesi, Fabio and Francesca Pratesi and S Rinzivillo and Salvatore Ruggieri and Siebes, Arno and Torra, Vicenc and Roberto Trasarti and Hoven, Jeroen van den and Vespignani, Alessandro}
}
@booklet {1425,
	title = {Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown},
	year = {2020},
	abstract = {Understanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the epidemics. In this report, we use mobile phone data to infer the movements of people between Italian provinces and municipalities, and we analyze the incoming, outcoming and internal mobility flows before and during the 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?},
	doi = {https://dx.doi.org/10.32079/ISTI-TR-2020/005},
	url = {https://arxiv.org/abs/2004.11278},
	author = {Pietro Bonato and Paolo Cintia and Francesco Fabbri and Daniele Fadda and Fosca Giannotti and Pier Luigi Lopalco and Sara Mazzilli and Mirco Nanni and Luca Pappalardo and Dino Pedreschi and Francesco Penone and S Rinzivillo and Giulio Rossetti and Marcello Savarese and Lara Tavoschi}
}
@conference {1046,
	title = {Discovering Mobility Functional Areas: A Mobility Data Analysis Approach},
	booktitle = {International Workshop on Complex Networks},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	abstract = {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.},
	doi = {10.1007/978-3-319-73198-8_27},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-73198-8_27},
	author = {Lorenzo Gabrielli and Daniele Fadda and Giulio Rossetti and Mirco Nanni and Piccinini, Leonardo and Dino Pedreschi and Fosca Giannotti and Patrizia Lattarulo}
}
@inbook {1422,
	title = {How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science},
	booktitle = {A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years},
	year = {2018},
	pages = {287 - 306},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {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.},
	isbn = {978-3-319-61893-7},
	doi = {https://doi.org/10.1007/978-3-319-61893-7_17},
	url = {https://link.springer.com/chapter/10.1007\%2F978-3-319-61893-7_17},
	author = {Amato, G. and Candela, L. and Castelli, D. and Esuli, A. and Falchi, F. and Gennaro, C. and Fosca Giannotti and Anna Monreale and Mirco Nanni and Pagano, P. and Luca Pappalardo and Dino Pedreschi and Francesca Pratesi and Rabitti, F. and S Rinzivillo and Giulio Rossetti and Salvatore Ruggieri and Sebastiani, F. and Tesconi, M.},
	editor = {Flesca, Sergio and Greco, Sergio and Masciari, Elio and Sacc{\`a}, Domenico}
}
@conference {953,
	title = {Clustering Individual Transactional Data for Masses of Users},
	booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
	year = {2017},
	publisher = {ACM},
	organization = {ACM},
	abstract = {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},
	doi = {10.1145/3097983.3098034},
	author = {Riccardo Guidotti and Anna Monreale and Mirco Nanni and Fosca Giannotti and Dino Pedreschi}
}
@article {959,
	title = {ICON Loop Carpooling Show Case},
	journal = {Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach},
	volume = {10101},
	year = {2017},
	pages = {310},
	abstract = {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.},
	url = {https://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf$\#$page=314},
	author = {Mirco Nanni and Lars Kotthoff and Riccardo Guidotti and Barry O{\textquoteright}Sullivan and Dino Pedreschi}
}
@article {955,
	title = {The Inductive Constraint Programming Loop},
	journal = {IEEE Intelligent Systems},
	year = {2017},
	abstract = {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.},
	doi = {10.1109/MIS.2017.265115706},
	author = {Bessiere, Christian and De Raedt, Luc and Tias Guns and Lars Kotthoff and Mirco Nanni and Siegfried Nijssen and Barry O{\textquoteright}Sullivan and Paparrizou, Anastasia and Dino Pedreschi and Simonis, Helmut}
}
@article {958,
	title = {The Inductive Constraint Programming Loop},
	journal = {Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach},
	volume = {10101},
	year = {2017},
	pages = {303},
	abstract = {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.},
	url = {https://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf$\#$page=307},
	author = {Mirco Nanni and Siegfried Nijssen and Barry O{\textquoteright}Sullivan and Paparrizou, Anastasia and Dino Pedreschi and Simonis, Helmut}
}
@conference {1030,
	title = {Movement Behaviour Recognition for Water Activities},
	booktitle = {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},
	year = {2017},
	doi = {10.1007/978-3-319-71970-2_7},
	url = {https://doi.org/10.1007/978-3-319-71970-2_7},
	author = {Mirco Nanni and Roberto Trasarti and Fosca Giannotti}
}
@article {956,
	title = {Never drive alone: Boosting carpooling with network analysis},
	journal = {Information Systems},
	volume = {64},
	year = {2017},
	pages = {237{\textendash}257},
	abstract = {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.},
	doi = {10.1016/j.is.2016.03.006},
	author = {Riccardo Guidotti and Mirco Nanni and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@article {1029,
	title = {Scalable and flexible clustering solutions for mobile phone-based population indicators},
	journal = {I. J. Data Science and Analytics},
	volume = {4},
	number = {4},
	year = {2017},
	pages = {285{\textendash}299},
	doi = {10.1007/s41060-017-0065-y},
	url = {https://doi.org/10.1007/s41060-017-0065-y},
	author = {Alessandro Lulli and Lorenzo Gabrielli and Patrizio Dazzi and Matteo Dell{\textquoteright}Amico and Pietro Michiardi and Mirco Nanni and Laura Ricci}
}
@conference {1031,
	title = {There{\textquoteright}s A Path For Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas},
	booktitle = {4th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017)},
	year = {2017},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Tokyo},
	author = {Riccardo Guidotti and Roberto Trasarti and Mirco Nanni and Fosca Giannotti and Dino Pedreschi}
}
@conference {834,
	title = {Big Data and Public Administration: a case study for Tuscany Airports},
	booktitle = {SEBD - Italian 	Symposium on  Advanced Database Systems },
	year = {2016},
	month = {06/2016},
	publisher = {Matematicamente.it},
	organization = {Matematicamente.it},
	address = {Ugento, Lecce (Italy)},
	abstract = {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.},
	isbn = {9788896354889},
	url = {http://sebd2016.unisalento.it/grid/SEBD2016-proceedings.pdf},
	author = {Barbara Furletti and Daniele Fadda and Leonardo Piccini and Mirco Nanni and Patrizia Lattarulo}
}
@article {875,
	title = {Driving Profiles Computation and Monitoring for Car Insurance CRM},
	journal = {Journal ACM Transactions on Intelligent Systems and Technology (TIST)},
	volume = {8},
	number = {1},
	year = {2016},
	pages = {14:1{\textendash}14:26},
	abstract = {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.},
	doi = {10.1145/2912148},
	url = {http://doi.acm.org/10.1145/2912148},
	author = {Mirco Nanni and Roberto Trasarti and Anna Monreale and Valerio Grossi and Dino Pedreschi}
}
@inbook {877,
	title = {Partition-Based Clustering Using Constraint Optimization},
	booktitle = {Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach},
	year = {2016},
	pages = {282{\textendash}299},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {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.},
	doi = {10.1007/978-3-319-50137-6_11},
	url = {http://dx.doi.org/10.1007/978-3-319-50137-6_11},
	author = {Valerio Grossi and Tias Guns and Anna Monreale and Mirco Nanni and Siegfried Nijssen}
}
@conference {878,
	title = {Clustering Formulation Using Constraint Optimization},
	booktitle = {Software Engineering and Formal Methods - {SEFM} 2015 Collocated Workshops: ATSE, HOFM, MoKMaSD, and VERY*SCART, York, UK, September 7-8, 2015, Revised Selected Papers},
	year = {2015},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	abstract = {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.},
	doi = {10.1007/978-3-662-49224-6_9},
	url = {http://dx.doi.org/10.1007/978-3-662-49224-6_9},
	author = {Valerio Grossi and Anna Monreale and Mirco Nanni and Dino Pedreschi and Franco Turini}
}
@conference {760,
	title = {Find Your Way Back: Mobility Profile Mining with Constraints},
	booktitle = {Principles and Practice of Constraint Programming},
	year = {2015},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cork},
	abstract = {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.},
	author = {Lars Kotthoff and Mirco Nanni and Riccardo Guidotti and Barry O{\textquoteright}Sullivan}
}
@conference {898,
	title = {{TOSCA:} two-steps clustering algorithm for personal locations detection},
	booktitle = {Proceedings of the 23rd {SIGSPATIAL} International Conference on Advances in Geographic Information Systems, Bellevue, WA, USA, November 3-6, 2015},
	year = {2015},
	doi = {10.1145/2820783.2820818},
	url = {http://doi.acm.org/10.1145/2820783.2820818},
	author = {Riccardo Guidotti and Roberto Trasarti and Mirco Nanni}
}
@conference {899,
	title = {Towards user-centric data management: individual mobility analytics for collective services},
	booktitle = {Proceedings of the 4th {ACM} {SIGSPATIAL} International Workshop on Mobile Geographic Information Systems, MobiGIS 2015, Bellevue, WA, USA, November 3-6, 2015},
	year = {2015},
	doi = {10.1145/2834126.2834132},
	url = {http://doi.acm.org/10.1145/2834126.2834132},
	author = {Riccardo Guidotti and Roberto Trasarti and Mirco Nanni and Fosca Giannotti}
}
@inbook {777,
	title = {Use of Mobile Phone Data to Estimate Visitors Mobility Flows},
	booktitle = {Software Engineering and Formal Methods},
	volume = {8938},
	number = {Lecture Notes in Computer Science},
	year = {2015},
	pages = {214-226},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {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 {\textquotedblleft}proxies{\textquotedblright}, 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.},
	issn = {978-3-319-15200-4},
	doi = {10.1007/978-3-319-15201-1_14},
	url = {http://link.springer.com/chapter/10.1007\%2F978-3-319-15201-1_14},
	author = {Lorenzo Gabrielli and Barbara Furletti and Fosca Giannotti and Mirco Nanni and S Rinzivillo}
}
@conference {574,
	title = {Big data analytics for smart mobility: a case study},
	booktitle = {EDBT/ICDT 2014 Workshops - Mining Urban Data (MUD)},
	year = {2014},
	month = {03/2014},
	address = {Athens, Greece},
	url = {http://ceur-ws.org/Vol-1133/paper-57.pdf},
	author = {Barbara Furletti and Roberto Trasarti and Lorenzo Gabrielli and Mirco Nanni and Dino Pedreschi}
}
@article {Trasarti2014,
	title = {Discovering urban and country dynamics from mobile phone data with spatial correlation patterns},
	journal = {Telecommunications Policy},
	year = {2014},
	pages = {-},
	abstract = {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.},
	keywords = {Urban dynamics},
	issn = {0308-5961},
	doi = {http://dx.doi.org/10.1016/j.telpol.2013.12.002},
	url = {http://www.sciencedirect.com/science/article/pii/S0308596113002012},
	author = {Roberto Trasarti and Ana-Maria Olteanu-Raimond and Mirco Nanni and Thomas Couronn{\'e} and Barbara Furletti and Fosca Giannotti and Zbigniew Smoreda and Cezary Ziemlicki}
}
@inbook {575,
	title = {Mobility Profiling},
	booktitle = {Data Science and Simulation in Transportation Research},
	year = {2014},
	pages = {1-29},
	publisher = {IGI Global},
	organization = {IGI Global},
	chapter = {1},
	abstract = {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. },
	doi = {10.4018/978-1-4666-4920-0.ch001},
	author = {Mirco Nanni and Roberto Trasarti and Paolo Cintia and Barbara Furletti and Chiara Renso and Lorenzo Gabrielli and S Rinzivillo and Fosca Giannotti}
}
@conference {725,
	title = {The purpose of motion: Learning activities from Individual Mobility Networks},
	booktitle = {International Conference on Data Science and Advanced Analytics, {DSAA} 2014, Shanghai, China, October 30 - November 1, 2014},
	year = {2014},
	doi = {10.1109/DSAA.2014.7058090},
	url = {http://dx.doi.org/10.1109/DSAA.2014.7058090},
	author = {S Rinzivillo and Lorenzo Gabrielli and Mirco Nanni and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti}
}
@conference {573,
	title = {Use of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach},
	booktitle = {47th SIS Scientific Meeting of the Italian Statistica Society},
	year = {2014},
	month = {06/2014},
	address = {Cagliari },
	abstract = {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 {\textquotedblleft}proxies{\textquotedblright}, as the mobile
calls data for mobility. In this paper we investigate to what extent such {\textquotedblright}big data{\textquotedblright},
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 {\textquotedblleft}Commssione di studio
avente il compito di orientare le scelte dellIstat sul tema dei Big Data {\textquotedblright}. In an on-
going project at ISTAT, called {\textquotedblleft}Persons and Places{\textquotedblright} {\textendash} based on an integration of
administrative data sources, it has been produced a first release of Origin Destina-
tion matrix {\textendash} at municipality level {\textendash} 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) {\textendash}
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)},
	isbn = {978-88-8467-874-4},
	url = {http://www.sis2014.it/proceedings/allpapers/3026.pdf},
	author = {Barbara Furletti and Lorenzo Gabrielli and Fosca Giannotti and Letizia Milli and Mirco Nanni and Dino Pedreschi}
}
@conference {MokMasd2014,
	title = {Use of mobile phone data to estimate visitors mobility flows},
	booktitle = {Proceedings of MoKMaSD},
	year = {2014},
	abstract = {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 {\textquotedblleft}proxies{\textquotedblright}, 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},
	url = {http://www.di.unipi.it/mokmasd/symposium-2014/preproceedings/GabrielliEtAl-mokmasd2014.pdf},
	author = {Lorenzo Gabrielli and Barbara Furletti and Fosca Giannotti and Mirco Nanni and S Rinzivillo}
}
@conference {704,
	title = {MP4-A Project: Mobility Planning For Africa},
	booktitle = {In D4D Challenge @ 3rd Conf. on the Analysis of Mobile Phone datasets (NetMob 2013)},
	year = {2013},
	address = {Cambridge, USA},
	abstract = {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.},
	url = {http://perso.uclouvain.be/vincent.blondel/netmob/2013/D4D-book.pdf},
	author = {Mirco Nanni and Roberto Trasarti and Barbara Furletti and Lorenzo Gabrielli and Peter Van Der Mede and Joost De Bruijn and Erik de Romph and Gerard Bruil}
}
@article {979,
	title = {Spatio-Temporal Data},
	journal = {Spatio-Temporal Databases: Flexible Querying and Reasoning},
	year = {2013},
	pages = {75},
	author = {Mirco Nanni and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {541,
	title = {A Study on Parameter Estimation for a Mining Flock Algorithm },
	booktitle = {Mining Complex Patterns Workshop, ECML PKDD 2013},
	year = {2013},
	author = {Rebecca Ong and Mirco Nanni and Chiara Renso and Monica Wachowicz and Dino Pedreschi}
}
@conference {683,
	title = {Transportation Planning Based on {GSM} Traces: {A} Case Study on Ivory Coast},
	booktitle = {Citizen in Sensor Networks - Second International Workshop, CitiSens 2013, Barcelona, Spain, September 19, 2013, Revised Selected Papers},
	year = {2013},
	doi = {10.1007/978-3-319-04178-0_2},
	url = {http://dx.doi.org/10.1007/978-3-319-04178-0_2},
	author = {Mirco Nanni and Roberto Trasarti and Barbara Furletti and Lorenzo Gabrielli and Peter Van Der Mede and Joost De Bruijn and Erik de Romph and Gerard Bruil}
}
@conference {687,
	title = {An Agent-Based Model to Evaluate Carpooling at Large Manufacturing Plants},
	booktitle = {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},
	year = {2012},
	doi = {10.1016/j.procs.2012.08.001},
	url = {http://dx.doi.org/10.1016/j.procs.2012.08.001},
	author = {Tom Bellemans and Sebastian Bothe and Sungjin Cho and Fosca Giannotti and Davy Janssens and Luk Knapen and Christine K{\"o}rner and Michael May and Mirco Nanni and Dino Pedreschi and Hendrik Stange and Roberto Trasarti and Ansar-Ul-Haque Yasar and Geert Wets}
}
@article {462,
	title = {Data Science for Simulating the Era of Electric Vehicles},
	journal = {KI - K{\"u}nstliche Intelligenz},
	year = {2012},
	doi = {10.1007/s13218-012-0183-6},
	author = {Davy Janssens and Fosca Giannotti and Mirco Nanni and Dino Pedreschi and S Rinzivillo}
}
@conference {686,
	title = {Individual Mobility Profiles: Methods and Application on Vehicle Sharing},
	booktitle = {Twentieth Italian Symposium on Advanced Database Systems, {SEBD} 2012, Venice, Italy, June 24-27, 2012, Proceedings},
	year = {2012},
	url = {http://sebd2012.dei.unipd.it/documents/188475/32d00b8a-8ead-4d97-923f-bd2f2cf6ddcb},
	author = {Roberto Trasarti and Fabio Pinelli and Mirco Nanni and Fosca Giannotti}
}
@conference {TrasartiPNG11,
	title = {Mining mobility user profiles for car pooling},
	booktitle = {KDD},
	year = {2011},
	pages = {1190-1198},
	author = {Roberto Trasarti and Fabio Pinelli and Mirco Nanni and Fosca Giannotti}
}
@article {TrasartiGNPR11,
	title = {A Query Language for Mobility Data Mining},
	journal = {IJDWM},
	volume = {7},
	number = {1},
	year = {2011},
	pages = {24-45},
	author = {Roberto Trasarti and Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Chiara Renso}
}
@conference {OngPTNRRG11,
	title = {Traffic Jams Detection Using Flock Mining},
	booktitle = {ECML/PKDD (3)},
	year = {2011},
	pages = {650-653},
	author = {Rebecca Ong and Fabio Pinelli and Roberto Trasarti and Mirco Nanni and Chiara Renso and S Rinzivillo and Fosca Giannotti}
}
@article {vlbdjMatlas,
	title = {Unveiling the complexity of human mobility by querying and mining massive trajectory data},
	journal = {VLDB J.},
	volume = {20},
	number = {5},
	year = {2011},
	pages = {695-719},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli and Chiara Renso and S Rinzivillo and Roberto Trasarti}
}
@conference {NanniTRGP10,
	title = {Advanced knowledge discovery on movement data with the GeoPKDD system},
	booktitle = {EDBT},
	year = {2010},
	pages = {693-696},
	author = {Mirco Nanni and Roberto Trasarti and Chiara Renso and Fosca Giannotti and Dino Pedreschi}
}
@conference {NanniTRGP10,
	title = {Advanced knowledge discovery on movement data with the GeoPKDD system},
	booktitle = {EDBT},
	year = {2010},
	pages = {693-696},
	author = {Mirco Nanni and Roberto Trasarti and Chiara Renso and Fosca Giannotti and Dino Pedreschi}
}
@conference {TrasartiRPNM10,
	title = {Exploring Real Mobility Data with M-Atlas},
	booktitle = {ECML/PKDD (3)},
	year = {2010},
	pages = {624-627},
	abstract = {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.},
	doi = {10.1007/978-3-642-15939-8_48},
	author = {Roberto Trasarti and S Rinzivillo and Fabio Pinelli and Mirco Nanni and Anna Monreale and Chiara Renso and Dino Pedreschi and Fosca Giannotti}
}
@conference {GiannottiNPPR10,
	title = {Mobility data mining: discovering movement patterns from trajectory data},
	booktitle = {Computational Transportation Science},
	year = {2010},
	pages = {7-10},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli and Chiara Renso and S Rinzivillo and Roberto Trasarti}
}
@conference {NanniT10,
	title = {Querying and mining trajectories with gaps: a multi-path reconstruction approach (Extended Abstract)},
	booktitle = {SEBD},
	year = {2010},
	pages = {126-133},
	author = {Mirco Nanni and Roberto Trasarti}
}
@inbook {KisilevichMNR10,
	title = {Spatio-temporal clustering},
	booktitle = {Data Mining and Knowledge Discovery Handbook},
	year = {2010},
	pages = {855-874},
	author = {Slava Kisilevich and Florian Mansmann and Mirco Nanni and S Rinzivillo}
}
@conference {fet2009,
	title = {GeoPKDD {\textendash} Geographic Privacy-aware Knowledge Discovery},
	booktitle = {The European Future Technologies Conference (FET 2009)},
	year = {2009},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Chiara Renso and S Rinzivillo and Roberto Trasarti}
}
@conference {NanniT09,
	title = {K-BestMatch Reconstruction and Comparison of Trajectory Data},
	booktitle = {ICDM Workshops},
	year = {2009},
	pages = {610-615},
	author = {Mirco Nanni and Roberto Trasarti}
}
@conference {NanniT09,
	title = {K-BestMatch Reconstruction and Comparison of Trajectory Data},
	booktitle = {ICDM Workshops},
	year = {2009},
	pages = {610-615},
	author = {Mirco Nanni and Roberto Trasarti}
}
@conference {GiannottiNPRT09,
	title = {Mining Mobility Behavior from Trajectory Data},
	booktitle = {CSE (4)},
	year = {2009},
	pages = {948-951},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Chiara Renso and Roberto Trasarti}
}
@conference {BerlingerioPNG09,
	title = {Temporal mining for interactive workflow data analysis},
	booktitle = {KDD},
	year = {2009},
	pages = {109-118},
	author = {Michele Berlingerio and Fabio Pinelli and Mirco Nanni and Fosca Giannotti}
}
@conference {DBLP:conf/gis/Gi,
	title = {Trajectory pattern analysis for urban traffic},
	booktitle = {Second International Workshop on Computational Transportation Science},
	year = {2009},
	month = {11/2009},
	pages = {43-47},
	publisher = {ACM},
	organization = {ACM},
	address = {SEATTLE, USA},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli}
}
@conference {AndrienkoARNP09,
	title = {A Visual Analytics Toolkit for Cluster-Based Classification of Mobility Data},
	booktitle = {SSTD},
	year = {2009},
	pages = {432-435},
	author = {Gennady Andrienko and Natalia Andrienko and S Rinzivillo and Mirco Nanni and Dino Pedreschi}
}
@conference {ClusterVAST,
	title = {Visual Cluster Analysis of Large Collections of Trajectories},
	booktitle = {IEEE Visual Analytics Science and Tecnology (VAST 2009)},
	year = {2009},
	publisher = {IEEE Computer Society Press},
	organization = {IEEE Computer Society Press},
	author = {Gennady Andrienko and Natalia Andrienko and S Rinzivillo and Mirco Nanni and Dino Pedreschi and Fosca Giannotti}
}
@inbook {NanniKKMP08,
	title = {Spatiotemporal Data Mining},
	booktitle = {Mobility, Data Mining and Privacy},
	year = {2008},
	pages = {267-296},
	author = {Mirco Nanni and Bart Kuijpers and Christine K{\"o}rner and Michael May and Dino Pedreschi}
}
@conference {DBLP:conf/sebd/BerlingerioGNP08,
	title = {Temporal analysis of process logs: a case study},
	booktitle = {SEBD},
	year = {2008},
	pages = {430-437},
	author = {Michele Berlingerio and Fosca Giannotti and Mirco Nanni and Fabio Pinelli}
}
@article {IV2008,
	title = {Visually driven analysis of movement data by progressive clustering},
	journal = {Information Visualization},
	volume = {7},
	number = {3-4},
	year = {2008},
	pages = {225-239},
	publisher = {Palgrave Macmillan Ltd},
	author = {S Rinzivillo and Dino Pedreschi and Mirco Nanni and Fosca Giannotti and Natalia Andrienko and Gennady Andrienko}
}
@conference {DBLP:conf/kdd/GiannottiNPP07,
	title = {Trajectory pattern mining},
	booktitle = {KDD},
	year = {2007},
	pages = {330-339},
	author = {Fosca Giannotti and Mirco Nanni and Fabio Pinelli and Dino Pedreschi}
}
@conference {DBLP:conf/sdm/GiannottiNP06,
	title = {Efficient Mining of Temporally Annotated Sequences},
	booktitle = {SDM},
	year = {2006},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi}
}
@conference {DBLP:conf/sac/GiannottiNPP06,
	title = {Mining sequences with temporal annotations},
	booktitle = {SAC},
	year = {2006},
	pages = {593-597},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli}
}
@article {DBLP:journals/jiis/NanniP06,
	title = {Time-focused clustering of trajectories of moving objects},
	journal = {J. Intell. Inf. Syst.},
	volume = {27},
	number = {3},
	year = {2006},
	pages = {267-289},
	author = {Mirco Nanni and Dino Pedreschi}
}
@conference {DBLP:conf/wlp/NanniRRT04,
	title = {Deductive and Inductive Reasoning on Spatio-Temporal Data},
	booktitle = {INAP/WLP},
	year = {2004},
	pages = {98-115},
	author = {Mirco Nanni and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/sebd/NanniRRT04,
	title = {Deductive and Inductive Reasoning on Trajectories},
	booktitle = {SEBD},
	year = {2004},
	pages = {98-105},
	author = {Mirco Nanni and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/dmkd/BacarellaGNP04,
	title = {Discovery of ads web hosts through traffic data analysis},
	booktitle = {DMKD},
	year = {2004},
	pages = {76-81},
	author = {V. Bacarella and Fosca Giannotti and Mirco Nanni and Dino Pedreschi}
}
@article {NRRT04,
	title = {\newblock{A Declarative Framework for Reasoning on Spatio-temporal Data}},
	year = {2004},
	note = {\newblock{Book chapter in Spatio-temporal databases, flexible querying and reasoning, R. de Caluwe, G. de Tr{\`e}, G. Bordogna editors, Physica Verlag }.},
	author = {Mirco Nanni and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/sebd/GiannottiNPS03,
	title = {WebCat: Automatic Categorization of Web Search Results},
	booktitle = {SEBD},
	year = {2003},
	pages = {507-518},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and F. Samaritani}
}
@conference {DBLP:conf/itcc/BonchiGMRNPR01,
	title = {Data Mining for Intelligent Web Caching},
	booktitle = {ITCC},
	year = {2001},
	pages = {599-603},
	author = {Francesco Bonchi and Fosca Giannotti and Giuseppe Manco and Chiara Renso and Mirco Nanni and Dino Pedreschi and Salvatore Ruggieri}
}
@conference {DBLP:conf/itcc/BonchiGMRNPR01,
	title = {Data Mining for Intelligent Web Caching},
	booktitle = {ITCC},
	year = {2001},
	pages = {599-603},
	author = {Francesco Bonchi and Fosca Giannotti and Giuseppe Manco and Chiara Renso and Mirco Nanni and Dino Pedreschi and Salvatore Ruggieri}
}
@article {DBLP:journals/tkde/GiannottiMNP01,
	title = {Nondeterministic, Nonmonotonic Logic Databases},
	journal = {IEEE Trans. Knowl. Data Eng.},
	volume = {13},
	number = {5},
	year = {2001},
	pages = {813-823},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi}
}
@article {DBLP:journals/dke/BonchiGGMNPRR01,
	title = {Web log data warehousing and mining for intelligent web caching},
	journal = {Data Knowl. Eng.},
	volume = {39},
	number = {2},
	year = {2001},
	pages = {165-189},
	author = {Francesco Bonchi and Fosca Giannotti and Cristian Gozzi and Giuseppe Manco and Mirco Nanni and Dino Pedreschi and Chiara Renso and Salvatore Ruggieri}
}
@article {DBLP:journals/dke/BonchiGGMNPRR01,
	title = {Web log data warehousing and mining for intelligent web caching},
	journal = {Data Knowl. Eng.},
	volume = {39},
	number = {2},
	year = {2001},
	pages = {165-189},
	author = {Francesco Bonchi and Fosca Giannotti and Cristian Gozzi and Giuseppe Manco and Mirco Nanni and Dino Pedreschi and Chiara Renso and Salvatore Ruggieri}
}
@article {BGGMNPRR01,
	title = {Web Log Data Warehousing and Mining for Intelligent Web Caching},
	journal = {Data and Knowledge Engineering},
	year = {2001},
	note = {39:165, November .},
	author = {Francesco Bonchi and Fosca Giannotti and Cristian Gozzi and Giuseppe Manco and Mirco Nanni and Dino Pedreschi and Chiara Renso and Salvatore Ruggieri}
}
@article {DBLP:journals/amai/FayzullinNPS00,
	title = {Foundations of distributed interaction systems},
	journal = {Ann. Math. Artif. Intell.},
	volume = {28},
	number = {1-4},
	year = {2000},
	pages = {127-168},
	author = {Marat Fayzullin and Mirco Nanni and Dino Pedreschi and V. S. Subrahmanian}
}
@conference {DBLP:conf/ejc/GiannottiNP00,
	title = {Logic-Based Knowledge Discovery in Databases},
	booktitle = {EJC},
	year = {2000},
	pages = {279-283},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi}
}
@conference {DBLP:conf/sebd/GiannottiMNPT99,
	title = {Integration of Deduction and Induction for Mining Supermarket Sales Data},
	booktitle = {SEBD},
	year = {1999},
	pages = {117-131},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/csl/GiannottiMNP98,
	title = {On the Effective Semantics of Nondeterministic, Nonmonotonic, Temporal Logic Databases},
	booktitle = {CSL},
	year = {1998},
	pages = {58-72},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi}
}
@conference {DBLP:conf/fqas/GiannottiMNP98,
	title = {Query Answering in Nondeterministic, Nonmonotonic Logic Databases},
	booktitle = {FQAS},
	year = {1998},
	pages = {175-187},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi}
}
@conference {DBLP:conf/dood/GiannottiMNP97,
	title = {Datalog++: A Basis for Active Object-Oriented Databases},
	booktitle = {DOOD},
	year = {1997},
	pages = {283-301},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi}
}
@conference {DBLP:conf/sebd/GiannottiMNP97,
	title = {Datalog++: a Basis for Active Object.Oriented Databases},
	booktitle = {SEBD},
	year = {1997},
	pages = {325-340},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi}
}
