@proceedings {1498,
	title = {Semantic Enrichment of XAI Explanations for Healthcare},
	year = {2022},
	abstract = {Explaining black-box models decisions is crucial to increase doctors{\textquoteright} trust in AI-based clinical decision support systems. However, current eXplainable Artificial Intelligence (XAI) techniques usually provide explanations that are not easily understandable by experts outside of AI. Furthermore, most of the them produce explanations that consider only the input features of the algorithm. However, broader information about the clinical context of a patient is usually available even if not processed by the AI-based clinical decision support system for its decision. Enriching the explanations with relevant clinical information concerning the health status of a patient would increase the ability of human experts to assess the reliability of the AI decision. Therefore, in this paper we present a methodology that aims to enable clinical reasoning by semantically enriching AI explanations. Starting from a medical AI explanation based only on the input features provided to the algorithm, our methodology leverages medical ontologies and NLP embedding techniques to link relevant information present in the patient{\textquoteright}s clinical notes to the original explanation. We validate our methodology with two experiments involving a human expert. Our results highlight promising performance in correctly identifying relevant information about the diseases of the patients, in particular about the associated morphology. This suggests that the presented methodology could be a first step toward developing a natural language explanation of AI decision support systems.},
	author = {Luca Corbucci and Anna Monreale and Cecilia Panigutti and Michela Natilli and Smiraglio, Simona and Dino Pedreschi}
}
@proceedings {1500,
	title = {SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics.},
	year = {2022},
	address = {Tirrenia, Pisa},
	abstract = {SoBigData RI has the ambition to support the rising demand for cross-disciplinary research and innovation on the multiple aspects of social complexity from combined data and model-driven perspectives and the increasing importance of ethics and data scientists{\textquoteright} responsibility as pillars of trustworthy use of Big Data and analytical technology. Digital traces of human activities offer a considerable opportunity to scrutinize the ground truth of individual and collective behaviour at an unprecedented detail and on a global scale. This increasing wealth of data is a chance to understand social complexity, provided we can rely on social mining, i.e., adequate means for accessing big social data and models for extracting knowledge from them. SoBigData RI, with its tools and services, empowers researchers and innovators through a platform for the design and execution of large-scale social mining experiments, open to users with diverse backgrounds, accessible on the cloud (aligned with EOSC), and also exploiting supercomputing facilities. Pushing the FAIR (Findable, Accessible, Interoperable) and FACT (Fair, Accountable, Confidential, and Transparent) principles will render social mining experiments more efficiently designed, adjusted, and repeatable by domain experts that are not data scientists. SoBigData RI moves forward from the simple awareness of ethical and legal challenges in social mining to the development of concrete tools that operationalize ethics with value-sensitive design, incorporating values and norms for privacy protection, fairness, transparency, and pluralism. SoBigData RI is the result of two H2020 grants (g.a. n.654024 and 871042), and it is part of the ESFRI 2021 Roadmap.},
	author = {Trasarti, Roberto and Grossi, Valerio and Michela Natilli and Rapisarda, Beatrice}
}
@article {1545,
	title = {Stable and actionable explanations of black-box models through factual and counterfactual rules},
	journal = {Data Mining and Knowledge Discovery},
	year = {2022},
	author = {Guidotti, Riccardo and Monreale, Anna and Ruggieri, Salvatore and Naretto, Francesca and Turini, Franco and Pedreschi, Dino and Giannotti, Fosca}
}
@article {1449,
	title = {STS-EPR: Modelling individual mobility considering the spatial, temporal, and social dimensions together},
	year = {2021},
	month = {05},
	doi = {10.1016/j.procs.2021.03.035},
	author = {Cornacchia, Giuliano and Luca Pappalardo}
}
@article {1302,
	title = {(So) Big Data and the transformation of the city},
	journal = {International Journal of Data Science and Analytics},
	year = {2020},
	abstract = {The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the {\textquotedblleft}City of Citizens{\textquotedblright} thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.},
	doi = {https://doi.org/10.1007/s41060-020-00207-3},
	url = {https://link.springer.com/article/10.1007/s41060-020-00207-3},
	author = {Andrienko, Gennady and Andrienko, Natalia and Boldrini, Chiara and Caldarelli, Guido and Paolo Cintia and Cresci, Stefano and Facchini, Angelo and Fosca Giannotti and Gionis, Aristides and Riccardo Guidotti and others}
}
@proceedings {1391,
	title = {SAI a Sensible Artificial Intelligence that plays Go},
	year = {2019},
	author = {F Morandin and G Amato and R Gini and C Metta and M Parton and G.C. Pascutto}
}
@book {1225,
	title = {Sar{\`o} Franco - Vita di Franco Turini, executive chef dell{\textquoteright}Universit{\`a} di Pisa},
	year = {2019},
	pages = {20},
	publisher = {Pisa University Press},
	organization = {Pisa University Press},
	address = {Pisa, Italy},
	abstract = {Chi {\`e} Franco Turini? Come molti sanno, uno dei pionieri dell{\textquoteright}informatica italiana. Ma non {\`e} questa la domanda che ci interessa. Quella a cui questo breve saggio si propone di rispondere {\`e} una questione molto pi{\`u} importante: chi avrebbe voluto essere Franco Turini?
In questo scritto, la vita e la carriera di Turini vengono ripercorse alla luce della sua vera, unica e irredimibile passione: la cucina. In un intreccio romanzesco, denso di colpi di scena e assolutamente falso e tendenzioso, il contributo di Franco Turini all{\textquoteright}informatica e all{\textquoteright}intelligenza artificiale si dipana, indissolubilmente intrecciato alla sua passione per i fornelli, attraverso le molte intuizioni geniali che lo hanno colpito mentre cucinava.},
	issn = {978-88-3339-252-3},
	url = {https://store.streetlib.com/it/marco-malvaldi/saro-franco-9788833392523/},
	author = {Marco Malvaldi}
}
@conference {1264,
	title = {On The Stability of Interpretable Models},
	booktitle = {2019 International Joint Conference on Neural Networks (IJCNN)},
	year = {2019},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process. Bias in data collection and preparation, or in model{\textquoteright}s construction may severely affect the accountability of the design process. We conduct an experimental study of the stability of interpretable models with respect to feature selection, instance selection, and model selection. Our conclusions should raise awareness and attention of the scientific community on the need of a stability impact assessment of interpretable models.},
	doi = {https://doi.org/10.1109/IJCNN.2019.8852158},
	url = {https://ieeexplore.ieee.org/abstract/document/8852158},
	author = {Riccardo Guidotti and Salvatore Ruggieri}
}
@conference {1053,
	title = {SoBigData: Social Mining \& Big Data Ecosystem},
	booktitle = {Companion of the The Web Conference 2018 on The Web Conference 2018},
	year = {2018},
	publisher = {International World Wide Web Conferences Steering Committee},
	organization = {International World Wide Web Conferences Steering Committee},
	abstract = {One of the most pressing and fascinating challenges scientists face today, is understanding the complexity of our globally interconnected society. The big data arising from the digital breadcrumbs of human activities has the potential of providing a powerful social microscope, which can help us understand many complex and hidden socio-economic phenomena. Such challenge requires high-level analytics, modeling and reasoning across all the social dimensions above. There is a need to harness these opportunities for scientific advancement and for the social good, compared to the currently prevalent exploitation of big data for commercial purposes or, worse, social control and surveillance. The main obstacle to this accomplishment, besides the scarcity of data scientists, is the lack of a large-scale open ecosystem where big data and social mining research can be carried out. The SoBigData Research Infrastructure (RI) provides an integrated ecosystem for ethic-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life as recorded by "big data". The research community uses the SoBigData facilities as a "secure digital wind-tunnel" for large-scale social data analysis and simulation experiments. SoBigData promotes repeatable and open science and supports data science research projects by providing: i) an ever-growing, distributed data ecosystem for procurement, access and curation and management of big social data, to underpin social data mining research within an ethic-sensitive context; ii) an ever-growing, distributed platform of interoperable, social data mining methods and associated skills: tools, methodologies and services for mining, analysing, and visualising complex and massive datasets, harnessing the techno-legal barriers to the ethically safe deployment of big data for social mining; iii) an ecosystem where protection of personal information and the respect for fundamental human rights can coexist with a safe use of the same information for scientific purposes of broad and central societal interest. SoBigData has a dedicated ethical and legal board, which is implementing a legal and ethical framework.},
	url = {http://www.sobigdata.eu/sites/default/files/www\%202018.pdf},
	author = {Fosca Giannotti and Roberto Trasarti and Bontcheva, Kalina and Valerio Grossi}
}
@article {1261,
	title = {A survey of methods for explaining black box models},
	journal = {ACM computing surveys (CSUR)},
	volume = {51},
	number = {5},
	year = {2018},
	pages = {93},
	abstract = {In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.},
	doi = {10.1145/3236009},
	url = {https://dl.acm.org/doi/abs/10.1145/3236009},
	author = {Riccardo Guidotti and Anna Monreale and Salvatore Ruggieri and Franco Turini and Fosca Giannotti and Dino Pedreschi}
}
@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}
}
@article {998,
	title = {Segregation discovery in a social network of companies},
	journal = {Journal of Intelligent Information Systems},
	year = {2017},
	month = {Sep},
	abstract = {We introduce a framework for the data-driven analysis of social segregation of minority groups, and challenge it on a complex scenario. The framework builds on quantitative measures of segregation, called segregation indexes, proposed in the social science literature. The segregation discovery problem is introduced, which consists of searching sub-groups of population and minorities for which a segregation index is above a minimum threshold. A search algorithm is devised that solves the segregation problem by computing a multi-dimensional data cube that can be explored by the analyst. The machinery underlying the search algorithm relies on frequent itemset mining concepts and tools. The framework is challenged on a cases study in the context of company networks. We analyse segregation on the grounds of sex and age for directors in the boards of the Italian companies. The network includes 2.15M companies and 3.63M directors.},
	issn = {1573-7675},
	doi = {10.1007/s10844-017-0485-0},
	url = {https://doi.org/10.1007/s10844-017-0485-0},
	author = {Alessandro Baroni and Salvatore Ruggieri}
}
@conference {1050,
	title = {Sentiment Spreading: An Epidemic Model for Lexicon-Based Sentiment Analysis on Twitter},
	booktitle = {Conference of the Italian Association for Artificial Intelligence},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	abstract = {While sentiment analysis has received significant attention in the last years, problems still exist when tools need to be applied to microblogging content. This because, typically, the text to be analysed consists of very short messages lacking in structure and semantic context. At the same time, the amount of text produced by online platforms is enormous. So, one needs simple, fast and effective methods in order to be able to efficiently study sentiment in these data. Lexicon-based methods, which use a predefined dictionary of terms tagged with sentiment valences to evaluate sentiment in longer sentences, can be a valid approach. Here we present a method based on epidemic spreading to automatically extend the dictionary used in lexicon-based sentiment analysis, starting from a reduced dictionary and large amounts of Twitter data. The resulting dictionary is shown to contain valences that correlate well with human-annotated sentiment, and to produce tweet sentiment classifications comparable to the original dictionary, with the advantage of being able to tag more tweets than the original. The method is easily extensible to various languages and applicable to large amounts of data.},
	doi = {10.1007/978-3-319-70169-1_9},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-70169-1_9},
	author = {Pollacci, Laura and Alina Sirbu and Fosca Giannotti and Dino Pedreschi and Claudio Lucchese and Muntean, Cristina Ioana}
}
@article {966,
	title = {Survey on using constraints in data mining},
	journal = {Data Mining and Knowledge Discovery},
	volume = {31},
	number = {2},
	year = {2017},
	pages = {424{\textendash}464},
	abstract = {This paper provides an overview of the current state-of-the-art on using constraints in knowledge discovery and data mining. The use of constraints in a data mining task requires specific definition and satisfaction tools during knowledge extraction. This survey proposes three groups of studies based on classification, clustering and pattern mining, whether the constraints are on the data, the models or the measures, respectively. We consider the distinctions between hard and soft constraint satisfaction, and between the knowledge extraction phases where constraints are considered. In addition to discussing how constraints can be used in data mining, we show how constraint-based languages can be used throughout the data mining process.},
	doi = {10.1007/s10618-016-0480-z},
	author = {Valerio Grossi and Andrea Romei and Franco Turini}
}
@conference {1177,
	title = {SPARQL Queries over Source Code},
	booktitle = {2016 IEEE Tenth International Conference on Semantic Computing (ICSC)},
	year = {2016},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Mattia Setzu and Atzori, Maurizio}
}
@article {897,
	title = {Special Issue on Mobile Traffic Analytics},
	journal = {Computer Communications},
	volume = {95},
	year = {2016},
	pages = {1{\textendash}2},
	abstract = {This Special Issue of Computer Communications is dedicated to mobile traffic data analysis. This is an emerging field of research that stems from the increasing pervasiveness in our lives of always-connected mobile devices. These devices continuously collect, generate, receive or communicate data; in doing so, they leave trails of digital crumbs that can be followed, recorded and analysed in many and varied ways, and for a number of different purposes.
From a data collection perspective, applications running on smartphones allow tracking user activities with extreme accuracy, in terms of mobility, context, and service usage. Yet, having individuals informedly install and run software that monitors their actions is not obvious; finding adequate incentives is equivalently complex. The other option is gathering mobile traffic data in the mobile network. This is an increasingly common practice for telecommunication operators: the collection of minimum information required for billing is giving way to in-depth inspection and recording of mobile service usages in space and time, and of traffic flows at the network edge and core. In this case, data access remains the major impediment, due to privacy and industrial secrecy reasons.

Despite the issues inherent to the data collection, the richness of knowledge that can be extracted from the aforementioned sources is such that actors in both academia and industry are putting significant effort in gathering, analysing and possibly making available mobile traffic data. Indeed, mobile traffic data typically contain information on large populations of individuals (from thousands to millions users) with high spatio-temporal granularity. The combination of accuracy and coverage is unprecedented, and it has proven key in validating theories and scaling up experimental studies in a number of research fields across many disciplines, including physics, sociology, epidemiology, transportation systems, and, of course, mobile networking.

As a result, we witness today a rapid growth of the literature that proposes or exploits mobile traffic analytics. Included in this Special Issue are eight papers that cover a significant portion of the different research topics in this area, ranging from data collection to the characterization of land use and mobile service consumption, from the inference and prediction of user mobility to the detection of malicious traffic. These papers were selected from 30 high-quality submissions after at least two rounds of reviews by experts and guest editors. The original submissions were received from five continents and a variety of countries, including Austria, Argentina, Belgium, Brazil, Chile, China, France, Germany, Italy, South Korea, Luxembourg, Pakistan, Saudi Arabia, Spain, Sweden, Tunisia, Turkey, USA. The accepted papers reflect this geographical heterogeneity, and are authored by researchers based in Europe, North and South America.},
	doi = {10.1016/j.comcom.2016.10.009},
	url = {http://dx.doi.org/10.1016/j.comcom.2016.10.009},
	author = {Marco Fiore and Zubair Shafiq and Zbigniew Smoreda and Razvan Stanica and Roberto Trasarti}
}
@article {866,
	title = {A supervised approach for intra-/inter-community interaction prediction in dynamic social networks},
	journal = {Social Network Analysis and Mining},
	volume = {6},
	number = {1},
	year = {2016},
	month = {09/2016},
	pages = {86},
	abstract = {Due to the growing availability of Internet services in the last decade, the interactions between people became more and more easy to establish. For example, we can have an intercontinental job interview, or we can send real-time multimedia content to any friend of us just owning a smartphone. All this kind of human activities generates digital footprints, that describe a complex, rapidly evolving, network structures. In such dynamic scenario, one of the most challenging tasks involves the prediction of future interactions between couples of actors (i.e., users in online social networks, researchers in collaboration networks). In this paper, we approach such problem by leveraging networks dynamics: to this extent, we propose a supervised learning approach which exploits features computed by time-aware forecasts of topological measures calculated between node pairs. Moreover, since real social networks are generally composed by weakly connected modules, we instantiate the interaction prediction problem in two disjoint applicative scenarios: intra-community and inter-community link prediction. Experimental results on real time-stamped networks show how our approach is able to reach high accuracy. Furthermore, we analyze the performances of our methodology when varying the typologies of features, community discovery algorithms and forecast methods.},
	issn = {1869-5469},
	doi = {10.1007/s13278-016-0397-y},
	url = {http://dx.doi.org/10.1007/s13278-016-0397-y},
	author = {Giulio Rossetti and Riccardo Guidotti and Ioanna Miliou and Dino Pedreschi and Fosca Giannotti}
}
@conference {981,
	title = {Segregation Discovery in a Social Network of Companies},
	booktitle = {International Symposium on Intelligent Data Analysis},
	year = {2015},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {We introduce a framework for a data-driven analysis of segregation of minority groups in social networks, and challenge it on a complex scenario. The framework builds on quantitative measures of segregation, called segregation indexes, proposed in the social science literature. The segregation discovery problem consists of searching sub-graphs and sub-groups for which a reference segregation index is above a minimum threshold. A search algorithm is devised that solves the segregation problem. The framework is challenged on the analysis of segregation of social groups in the boards of directors of the real and large network of Italian companies connected through shared directors.},
	doi = {10.1007/978-3-319-24465-5_4},
	author = {Alessandro Baroni and Salvatore Ruggieri}
}
@article {724,
	title = {Small Area Model-Based Estimators Using Big Data Sources},
	journal = {Journal of Official Statistics},
	volume = {31},
	number = {2},
	year = {2015},
	pages = {263{\textendash}281},
	author = {Stefano Marchetti and Caterina Giusti and Monica Pratesi and Nicola Salvati and Fosca Giannotti and Dino Pedreschi and S Rinzivillo and Luca Pappalardo and Lorenzo Gabrielli}
}
@conference {823,
	title = {Social or green? A data-driven approach for more enjoyable carpooling},
	booktitle = {Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Riccardo Guidotti and Sassi, Andrea and Michele Berlingerio and Pascale, Alessandra and Ghaddar, Bissan}
}
@article {526,
	title = {Scalable Analysis of Movement Data for Extracting and Exploring Significant Places},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {19},
	number = {7},
	year = {2013},
	chapter = {49},
	author = {Gennady Andrienko and Natalia Andrienko and C. Hunter and S Rinzivillo and Stefan Wrobel}
}
@article {525,
	title = {Semantic Trajectories Modeling and Analysis},
	journal = {ACM Computing Surveys},
	volume = {45},
	number = {4},
	year = {2013},
	month = {August 2013},
	author = {Christine Parent and Stefano Spaccapietra and Chiara Renso and Gennady Andrienko and Natalia Andrienko and Vania Bogorny and Damiani M L, and Gkoulalas-Divanis A, and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Nikos Pelekis}
}
@article {527,
	title = {Spatial and Temporal Evaluation of Network-based Analysis of Human Mobility},
	journal = {Social Network Analysis and Mining},
	volume = {to appear},
	year = {2013},
	author = {Michele Coscia and S Rinzivillo and Fosca Giannotti and Dino Pedreschi}
}
@inbook {543,
	title = {Spatio and Spatio-temporal Reasoning and Decision Support Tools},
	booktitle = {Entry at Encyclopedia of Social Network Analysis and Mining},
	year = {2013},
	edition = {springer},
	author = {Monica Wachowicz and Chiara Renso}
}
@conference {503,
	title = {Spatio temporal keyword-queries in Social Networs},
	year = {2013},
	author = {Vittoria Cozza and Antonio Messina and Danilo Montesi and Luca Arietta and Matteo Magnani}
}
@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}
}
@article {601,
	title = {Smart cities of the future},
	journal = {European Physical Journal-Special Topics},
	volume = {214},
	number = {1},
	year = {2012},
	pages = {481},
	abstract = {Here we sketch the rudiments of what constitutes a smart city which we define as a city in which ICT is merged with traditional infrastructures, coordinated and integrated using new digital technologies. We first sketch our vision defining seven goals which concern: developing a new understanding of urban problems; effective and feasible ways to coordinate urban technologies; models and methods for using urban data across spatial and temporal scales; developing new technologies for communication and dissemination; developing new forms of urban governance and organisation; defining critical problems relating to cities, transport, and energy; and identifying risk, uncertainty, and hazards in the smart city. To this, we add six research challenges: to relate the infrastructure of smart cities to their operational functioning and planning through management, control and optimisation; to explore the notion of the city as a laboratory for innovation; to provide portfolios of urban simulation which inform future designs; to develop technologies that ensure equity, fairness and realise a better quality of city life; to develop technologies that ensure informed participation and create shared knowledge for democratic city governance; and to ensure greater and more effective mobility and access to opportunities for urban populations. We begin by defining the state of the art, explaining the science of smart cities. We define six scenarios based on new cities badging themselves as smart, older cities regenerating themselves as smart, the development of science parks, tech cities, and technopoles focused on high technologies, the development of urban services using contemporary ICT, the use of ICT to develop new urban intelligence functions, and the development of online and mobile forms of participation. Seven project areas are then proposed: Integrated Databases for the Smart City, Sensing, Networking and the Impact of New Social Media, Modelling Network Performance, Mobility and Travel Behaviour, Modelling Urban Land Use, Transport and Economic Interactions, Modelling Urban Transactional Activities in Labour and Housing Markets, Decision Support as Urban Intelligence, Participatory Governance and Planning Structures for the Smart City. Finally we anticipate the paradigm shifts that will occur in this research and define a series of key demonstrators which we believe are important to progressing a science of smart cities.},
	doi = {10.1140/epjst/e2012-01703-3},
	author = {Batty, Michael and Axhausen, Kay W and Fosca Giannotti and Pozdnoukhov, Alexei and Bazzani, Armando and Monica Wachowicz and Ouzounis, Georgios and Portugali, Yuval}
}
@conference {446,
	title = {Scalable Link Prediction on Multidimensional Networks},
	booktitle = {ICDM Workshops},
	year = {2011},
	pages = {979-986},
	address = {Vancouver},
	author = {Giulio Rossetti and Michele Berlingerio and Fosca Giannotti}
}
@inbook {811,
	title = {Stages of Gene Regulatory Network Inference: the Evolutionary Algorithm Role},
	booktitle = {Evolutionary Algorithms},
	year = {2011},
	publisher = {InTech},
	organization = {InTech},
	doi = {DOI: 10.5772/15182},
	url = {http://www.intechopen.com/articles/show/title/stages-of-gene-regulatory-network-inference-the-evolutionary-algorithm-role},
	author = {Alina Sirbu and Heather J Ruskin and Martin Crane}
}
@article {1278,
	title = {Stiramenti identitari. Strategie di integrazione degli strannieri nella provincia di Massa Carrara tra appartenenza etnica ed esperienza transnazionale},
	year = {2011},
	author = {Tomei, Gabriele and Paletti, F and Michela Natilli}
}
@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 {asonam09,
	title = {Social Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography},
	booktitle = {ASONAM},
	year = {2009},
	pages = {279-283},
	author = {Michele Coscia and Fosca Giannotti and Ruggero G. Pensa}
}
@conference {CosciaGP09,
	title = {Social Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography},
	booktitle = {ASONAM},
	year = {2009},
	pages = {279-283},
	author = {Michele Coscia and Fosca Giannotti and Ruggero G. Pensa}
}
@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/gis/GiannottiMPR05,
	title = {Synthetic generation of cellular network positioning data},
	booktitle = {GIS},
	year = {2005},
	pages = {12-20},
	author = {Fosca Giannotti and Andrea Mazzoni and Simone Puntoni and Chiara Renso}
}
@conference {DBLP:conf/gis/GiannottiMPR05,
	title = {Synthetic generation of cellular network positioning data},
	booktitle = {GIS},
	year = {2005},
	pages = {12-20},
	author = {Fosca Giannotti and Andrea Mazzoni and Simone Puntoni and Chiara Renso}
}
@article {DBLP:journals/tkde/GiannottiMT04,
	title = {Specifying Mining Algorithms with Iterative User-Defined Aggregates},
	journal = {IEEE Trans. Knowl. Data Eng.},
	volume = {16},
	number = {10},
	year = {2004},
	pages = {1232-1246},
	author = {Fosca Giannotti and Giuseppe Manco and Franco Turini}
}
@article {DBLP:journals/jcss/GiannottiPZ01,
	title = {Semantics and Expressive Power of Nondeterministic Constructs in Deductive Databases},
	journal = {J. Comput. Syst. Sci.},
	volume = {62},
	number = {1},
	year = {2001},
	pages = {15-42},
	author = {Fosca Giannotti and Dino Pedreschi and Carlo Zaniolo}
}
@conference {DBLP:conf/pkdd/GiannottiMT01,
	title = {Specifying Mining Algorithms with Iterative User-Defined Aggregates: A Case Study},
	booktitle = {PKDD},
	year = {2001},
	pages = {128-139},
	author = {Fosca Giannotti and Giuseppe Manco and Franco Turini}
}
@conference {DBLP:conf/dbpl/AmatoGM97,
	title = {Static Analysis of Transactions for Conservative Multigranularity Locking},
	booktitle = {DBPL},
	year = {1997},
	pages = {413-430},
	author = {Giuseppe Amato and Fosca Giannotti and Gianni Mainetto}
}
@conference {DBLP:conf/deductive/CarboniDGM96,
	title = {Spatio-Temporal Reasoning with LDLT: First Steps Towards a Deductive System for Geographical Applications},
	booktitle = {DDLP},
	year = {1996},
	pages = {135-151},
	author = {Marilisa E. Carboni and Annalisa Di Deo and Fosca Giannotti and Maria V Masserotti}
}
@conference {DBLP:conf/fmldo/AmatoGM93,
	title = {Static Analysis of Transactions: an Experiment of Abstract Interpretation Usage},
	booktitle = {FMLDO},
	year = {1993},
	pages = {19-29},
	author = {Giuseppe Amato and Fosca Giannotti and Gianni Mainetto}
}
@article {DBLP:journals/scp/GiannottiMPT87,
	title = {Symbolic Evaluation with Structural Recursive Symbolic Constants},
	journal = {Sci. Comput. Program.},
	volume = {9},
	number = {2},
	year = {1987},
	pages = {161-177},
	author = {Fosca Giannotti and Attilio Matteucci and Dino Pedreschi and Franco Turini}
}
@article {DBLP:journals/tse/AmbriolaGPT85,
	title = {Symbolic Semantics and Program Reduction},
	journal = {IEEE Trans. Software Eng.},
	volume = {11},
	number = {8},
	year = {1985},
	pages = {784-794},
	author = {Vincenzo Ambriola and Fosca Giannotti and Dino Pedreschi and Franco Turini}
}
