@article {1401,
	title = {GLocalX - From Local to Global Explanations of Black Box AI Models},
	volume = {294},
	year = {2021},
	month = {2021/05/01/},
	pages = {103457},
	abstract = {Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are {\textquotedblleft}black boxes{\textquotedblright} which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating {\textquotedblleft}local{\textquotedblright} explanations. We present GLocalX, a {\textquotedblleft}local-first{\textquotedblright} model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLocalX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLocalX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLocalX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications.},
	isbn = {0004-3702},
	doi = {https://doi.org/10.1016/j.artint.2021.103457},
	url = {https://www.sciencedirect.com/science/article/pii/S0004370221000084},
	author = {Mattia Setzu and Riccardo Guidotti and Anna Monreale and Franco Turini and Dino Pedreschi and Fosca Giannotti}
}
@article {1428,
	title = {Bias in data-driven artificial intelligence systems{\textemdash}An introductory survey},
	journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
	volume = {10},
	number = {3},
	year = {2020},
	pages = {e1356},
	abstract = {Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth.},
	doi = {https://doi.org/10.1002/widm.1356},
	url = {https://onlinelibrary.wiley.com/doi/full/10.1002/widm.1356},
	author = {Ntoutsi, Eirini and Fafalios, Pavlos and Gadiraju, Ujwal and Iosifidis, Vasileios and Nejdl, Wolfgang and Vidal, Maria-Esther and Salvatore Ruggieri and Franco Turini and Papadopoulos, Symeon and Krasanakis, Emmanouil and others}
}
@conference {1426,
	title = {Global Explanations with Local Scoring},
	booktitle = {Machine Learning and Knowledge Discovery in Databases},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Artificial Intelligence systems often adopt machine learning models encoding complex algorithms with potentially unknown behavior. As the application of these {\textquotedblleft}black box{\textquotedblright} models grows, it is our responsibility to understand their inner working and formulate them in human-understandable explanations. To this end, we propose a rule-based model-agnostic explanation method that follows a local-to-global schema: it generalizes a global explanation summarizing the decision logic of a black box starting from the local explanations of single predicted instances. We define a scoring system based on a rule relevance score to extract global explanations from a set of local explanations in the form of decision rules. Experiments on several datasets and black boxes show the stability, and low complexity of the global explanations provided by the proposed solution in comparison with baselines and state-of-the-art global explainers.},
	isbn = {978-3-030-43823-4},
	doi = {https://doi.org/10.1007/978-3-030-43823-4_14},
	url = {https://link.springer.com/chapter/10.1007\%2F978-3-030-43823-4_14},
	author = {Mattia Setzu and Riccardo Guidotti and Anna Monreale and Franco Turini},
	editor = {Cellier, Peggy and Driessens, Kurt}
}
@article {1283,
	title = {Factual and Counterfactual Explanations for Black Box Decision Making},
	journal = {IEEE Intelligent Systems},
	year = {2019},
	abstract = {The rise of sophisticated machine learning models has brought accurate but obscure decision systems, which hide their logic, thus undermining transparency, trust, and the adoption of artificial intelligence (AI) in socially sensitive and safety-critical contexts. We introduce a local rule-based explanation method, providing faithful explanations of the decision made by a black box classifier on a specific instance. The proposed method first learns an interpretable, local classifier on a synthetic neighborhood of the instance under investigation, generated by a genetic algorithm. Then, it derives from the interpretable classifier an explanation consisting of a decision rule, explaining the factual reasons of the decision, and a set of counterfactuals, suggesting the changes in the instance features that would lead to a different outcome. Experimental results show that the proposed method outperforms existing approaches in terms of the quality of the explanations and of the accuracy in mimicking the black box.},
	doi = {10.1109/MIS.2019.2957223},
	url = {https://ieeexplore.ieee.org/abstract/document/8920138},
	author = {Riccardo Guidotti and Anna Monreale and Fosca Giannotti and Dino Pedreschi and Salvatore Ruggieri and Franco Turini}
}
@conference {1215,
	title = {Meaningful explanations of Black Box AI decision systems},
	booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
	year = {2019},
	abstract = {Black box AI systems for automated decision making, often based on machine learning over (big) data, map a user{\textquoteright}s features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We focus on the urgent open challenge of how to construct meaningful explanations of opaque AI/ML systems, introducing the local-toglobal framework for black box explanation, articulated along three lines: (i) the language for expressing explanations in terms of logic rules, with statistical and causal interpretation; (ii) the inference of local explanations for revealing the decision rationale for a specific case, by auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of many local explanations into simple global ones, with algorithms that optimize for quality and comprehensibility. We argue that the local-first approach opens the door to a wide variety of alternative solutions along different dimensions: a variety of data sources (relational, text, images, etc.), a variety of learning problems (multi-label classification, regression, scoring, ranking), a variety of languages for expressing meaningful explanations, a variety of means to audit a black box.},
	doi = {10.1609/aaai.v33i01.33019780},
	url = {https://aaai.org/ojs/index.php/AAAI/article/view/5050},
	author = {Dino Pedreschi and Fosca Giannotti and Riccardo Guidotti and Anna Monreale and Salvatore Ruggieri and Franco Turini}
}
@article {1131,
	title = {Local Rule-Based Explanations of Black Box Decision Systems},
	year = {2018},
	author = {Riccardo Guidotti and Anna Monreale and Salvatore Ruggieri and Dino Pedreschi and Franco Turini and Fosca Giannotti}
}
@article {1132,
	title = {Open the Black Box Data-Driven Explanation of Black Box Decision Systems},
	year = {2018},
	author = {Dino Pedreschi and Fosca Giannotti and Riccardo Guidotti and Anna Monreale and Luca Pappalardo and Salvatore Ruggieri and Franco Turini}
}
@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 {1052,
	title = {An empirical verification of a-priori learning models on mailing archives in the context of online learning activities of participants in free\libre open source software (FLOSS) communities},
	journal = {Education and Information Technologies},
	volume = {22},
	number = {6},
	year = {2017},
	pages = {3207{\textendash}3229},
	abstract = {Free\Libre Open Source Software (FLOSS) environments are increasingly dubbed as learning environments where practical software engineering skills can be acquired. Numerous studies have extensively investigated how knowledge is acquired in these environments through a collaborative learning model that define a learning process. Such a learning process, identified either as a result of surveys or by means of questionnaires, can be depicted through a series of graphical representations indicating the steps FLOSS community members go through as they acquire and exchange skills. These representations are referred to as a-priori learning models. They are Petri net-like workflow nets (WF-net) that provide a visual representation of the learning process as it is expected to occur. These models are representations of a learning framework or paradigm in FLOSS communities. As such, the credibility of any models is estimated through a process of model verification and validation. Therefore in this paper, we analyze these models in comparison with the real behavior captured in FLOSS repositories by means of conformance verification in process mining. The purpose of our study is twofold. Firstly, the results of our analysis provide insights on the possible discrepancies that are observed between the initial theoretical representations of learning processes and the real behavior captured in FLOSS event logs, constructed from mailing archives. Secondly, this comparison helps foster the understanding on how learning actually takes place in FLOSS environments based on empirical evidence directly from the data.},
	doi = {10.1007/s10639-017-9573-6},
	url = {https://link.springer.com/article/10.1007/s10639-017-9573-6},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@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 {968,
	title = {Classification Rule Mining Supported by Ontology for Discrimination Discovery},
	booktitle = {Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on},
	year = {2016},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Discrimination discovery from data consists of designing data mining methods for the actual discovery of discriminatory situations and practices hidden in a large amount of historical decision records. Approaches based on classification rule mining consider items at a flat concept level, with no exploitation of background knowledge on the hierarchical and inter-relational structure of domains. On the other hand, ontologies are a widespread and ever increasing means for expressing such a knowledge. In this paper, we propose a framework for discrimination discovery from ontologies, where contexts of prima-facie evidence of discrimination are summarized in the form of generalized classification rules at different levels of abstraction. Throughout the paper, we adopt a motivating and intriguing case study based on discriminatory tariffs applied by the U. S. Harmonized Tariff Schedules on imported goods.},
	doi = {10.1109/ICDMW.2016.0128},
	author = {Luong, Binh Thanh and Salvatore Ruggieri and Franco Turini}
}
@inbook {965,
	title = {Data Mining and Constraints: An Overview},
	booktitle = {Data Mining and Constraint Programming},
	year = {2016},
	pages = {25{\textendash}48},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	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 requires mechanisms for defining and evaluating them during the knowledge extraction process. We give a structured account of three main groups of constraints based on the specific context in which they are defined and used. The aim is to provide a complete view on constraints as a building block of data mining methods.},
	doi = {10.1007/978-3-319-50137-6_2},
	author = {Valerio Grossi and Dino Pedreschi and Franco Turini}
}
@conference {969,
	title = {A KDD process for discrimination discovery},
	booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
	year = {2016},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {The acceptance of analytical methods for discrimination discovery by practitioners and legal scholars can be only achieved if the data mining and machine learning communities will be able to provide case studies, methodological refinements, and the consolidation of a KDD process. We summarize here an approach along these directions.},
	doi = {10.1007/978-3-319-46131-1_28},
	author = {Salvatore Ruggieri and Franco Turini}
}
@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}
}
@booklet {974,
	title = {An exploration of learning processes as process maps in FLOSS repositories},
	year = {2015},
	abstract = {Evidence suggests that Free/Libre Open Source Software (FLOSS) environ-ments provide unlimited learning opportunities. Community members engage in a number of activities both during their interaction with their peers and while mak-ing use of the tools available in these environments. A number of studies docu-ment the existence of learning processes in FLOSS through the analysis of sur-veys and questionnaires filled by FLOSS project participants. At the same time, the interest in understanding the dynamics of the FLOSS phenomenon, its popu-larity and success resulted in the development of tools and techniques for extract-ing and analyzing data from different FLOSS data sources. This new field is called Mining Software Repositories (MSR). In spite of these efforts, there is limited work aiming to provide empirical evidence of learning processes directly from FLOSS repositories. In this paper, we seek to trigger such an initiative by proposing an approach based on Process Mining to trace learning behaviors from FLOSS participants{\textquoteright} trails of activities, as recorded in FLOSS repositories, and visualize them as pro-cess maps. Process maps provide a pictorial representation of real behavior as it is recorded in FLOSS data. Our aim is to provide critical evidence that boosts the understanding of learning behavior in FLOSS communities by analyzing the rel-evant repositories. In order to accomplish this, we propose an effective approach that comprises first the mining of FLOSS repositories in order to generate Event logs, and then the generation of process maps, equipped with relevant statistical data interpreting and indicating the value of process discovery from these reposi-tories.},
	url = {http://eprints.adm.unipi.it/id/eprint/2344},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@conference {973,
	title = {The layered structure of company share networks},
	booktitle = {Data Science and Advanced Analytics (DSAA), 2015. 36678 2015. IEEE International Conference on},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a framework for the analysis of corporate governance problems using network science and graph algorithms on ownership networks. In such networks, nodes model companies/shareholders and edges model shares owned. Inspired by the widespread pyramidal organization of corporate groups of companies, we model ownership networks as layered graphs, and exploit the layered structure to design feasible and efficient solutions to three key problems of corporate governance. The first one is the long-standing problem of computing direct and indirect ownership (integrated ownership problem). The other two problems are introduced here: computing direct and indirect dividends (dividend problem), and computing the group of companies controlled by a parent shareholder (corporate group problem). We conduct an extensive empirical analysis of the Italian ownership network, which, with its 3.9M nodes, is 30{\texttimes} the largest network studied so far.},
	doi = {10.1109/DSAA.2015.7344809},
	author = {Andrea Romei and Salvatore Ruggieri and Franco Turini}
}
@conference {975,
	title = {Mining learning processes from FLOSS mailing archives},
	booktitle = {Conference on e-Business, e-Services and e-Society},
	year = {2015},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Evidence suggests that Free/Libre Open Source Software (FLOSS) environments provide unlimited learning opportunities. Community members engage in a number of activities both during their interaction with their peers and while making use of these environments. As FLOSS repositories store data about participants{\textquoteright} interaction and activities, we analyze participants{\textquoteright} interaction and knowledge exchange in emails to trace learning activities that occur in distinct phases of the learning process. We make use of semantic search in SQL to retrieve data and build corresponding event logs which are then fed to a process mining tool in order to produce visual workflow nets. We view these nets as representative of the traces of learning activities in FLOSS as well as their relevant flow of occurrence. Additional statistical details are provided to contextualize and describe these models.},
	doi = {10.1007/978-3-319-25013-7_23},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@conference {978,
	title = {An abstract state machine (ASM) representation of learning process in FLOSS communities},
	booktitle = {International Conference on Software Engineering and Formal Methods},
	year = {2014},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Free/Libre Open Source Software (FLOSS) communities as collaborative environments enable the occurrence of learning between participants in these groups. With the increasing interest research on understanding the mechanisms and processes through which learning occurs in FLOSS, there is an imperative to describe these processes. One successful way of doing this is through specification methods. In this paper, we describe the adoption of Abstract States Machines (ASMs) as a specification methodology for the description of learning processes in FLOSS. The goal of this endeavor is to represent the many possible steps and/or activities FLOSS participants go through during interactions that can be categorized as learning processes. Through ASMs, we express learning phases as states while activities that take place before moving from one state to another are expressed as transitions.},
	doi = {10.1007/978-3-319-15201-1_15},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@conference {977,
	title = {Ontolifloss: Ontology for learning processes in FLOSS communities},
	booktitle = {International Conference on Software Engineering and Formal Methods},
	year = {2014},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Free/Libre Open Source Software (FLOSS) communities are considered an example of commons-based peer-production models where groups of participants work together to achieve projects of common purpose. In these settings, many occurring activities can be documented and have established them as learning environments. As knowledge exchange is proved to occur in FLOSS, the dynamic and free nature of participation poses a great challenge in understanding activities pertaining to Learning Processes.

In this paper we raise this question and propose an ontology (called OntoLiFLOSS) in order to define terms and concepts that can explain learning activities taking place in these communities. The objective of this endeavor is to define in the simplest possible way a common definition of concepts and activities that can guide the identification of learning processes taking place among FLOSS members in any of the standard repositories such as mailing list, SVN, bug trackers and even discussion forums.},
	doi = {10.1007/978-3-319-15201-1_11},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@conference {976,
	title = {Process mining event logs from FLOSS data: state of the art and perspectives},
	booktitle = {International Conference on Software Engineering and Formal Methods},
	year = {2014},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Free/Libre Open Source Software (FLOSS) is a phenomenon that has undoubtedly triggered extensive research endeavors. At the heart of these initiatives is the ability to mine data from FLOSS repositories with the hope of revealing empirical evidence to answer existing questions on the FLOSS development process. In spite of the success produced with existing mining techniques, emerging questions about FLOSS data require alternative and more appropriate ways to explore and analyse such data.

In this paper, we explore a different perspective called process mining. Process mining has been proved to be successful in terms of tracing and reconstructing process models from data logs (event logs). The chief objective of our analysis is threefold. We aim to achieve: (1) conformance to predefined models; (2) discovery of new model patterns; and, finally, (3) extension to predefined models.

},
	doi = {10.1007/978-3-319-15201-1_12},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@inbook {634,
	title = {The discovery of discrimination},
	booktitle = {Discrimination and privacy in the information society},
	year = {2013},
	pages = {91{\textendash}108},
	publisher = {Springer},
	organization = {Springer},
	author = {Dino Pedreschi and Salvatore Ruggieri and Franco Turini}
}
@article {632,
	title = {Discrimination discovery in scientific project evaluation: A case study},
	journal = {Expert Systems with Applications},
	volume = {40},
	number = {15},
	year = {2013},
	pages = {6064{\textendash}6079},
	author = {Andrea Romei and Salvatore Ruggieri and Franco Turini}
}
@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}
}
@article {475,
	title = {Knowledge Discovery in Ontologies},
	journal = {Intelligent Data Analysis},
	volume = {16},
	year = {2012},
	chapter = {513},
	issn = {1571-4128},
	doi = {10.3233/IDA-2012-0536},
	url = {http://iospress.metapress.com/content/765h53w41286p578/fulltext.pdf},
	author = {Barbara Furletti and Franco Turini}
}
@inbook {476,
	title = {What else can be extracted from ontologies? Influence Rules},
	booktitle = {Software and Data Technologies},
	series = {Communications in Computer and Information Science},
	year = {2012},
	publisher = {Springer},
	organization = {Springer},
	author = {Franco Turini and Barbara Furletti}
}
@conference {474,
	title = {Mining Influence Rules out of Ontologies},
	booktitle = {International Conference on Software and Data Technologies (ICSOFT)},
	year = {2011},
	month = {2011},
	address = {Siviglia, Spagna},
	author = {Barbara Furletti and Franco Turini}
}
@article {473,
	title = {Improving the Business Plan Evaluation Process: the Role of Intangibles},
	journal = {Quality Technology \& Quantitative Management},
	volume = {7},
	number = {1},
	year = {2010},
	month = {2010},
	chapter = {35},
	issn = {1684-3703},
	url = {http://web.it.nctu.edu.tw/~qtqm/upcomingpapers/2010V7N1/2010V7N1_F3.pdf},
	author = {Barbara Furletti and Franco Turini and Andrea Bellandi and Miriam Baglioni and Chiara Pratesi}
}
@conference {PedreschiRT09,
	title = {Integrating induction and deduction for finding evidence of discrimination},
	booktitle = {ICAIL},
	year = {2009},
	pages = {157-166},
	author = {Dino Pedreschi and Salvatore Ruggieri and Franco Turini}
}
@conference {sdmPedreschiRT09,
	title = {Measuring Discrimination in Socially-Sensitive Decision Records},
	booktitle = {SDM},
	year = {2009},
	pages = {581-592},
	author = {Dino Pedreschi and Salvatore Ruggieri and Franco Turini}
}
@inbook {BerlingerioBCGT9,
	title = {Mining Clinical, Immunological, and Genetic Data of Solid Organ Transplantation},
	booktitle = {Biomedical Data and Applications},
	year = {2009},
	pages = {211-236},
	author = {Michele Berlingerio and Francesco Bonchi and Michele Curcio and Fosca Giannotti and Franco Turini}
}
@article {DBLP:journals/geoinformatica/RaffaetaCCGMPRST08,
	title = {An Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology},
	journal = {GeoInformatica},
	volume = {12},
	number = {1},
	year = {2008},
	pages = {37-72},
	author = {Alessandra Raffaet{\`a} and T. Ceccarelli and D. Centeno and Fosca Giannotti and A. Massolo and Christine Parent and Chiara Renso and Stefano Spaccapietra and Franco Turini}
}
@article {geoinfo,
	title = {An Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology},
	year = {2008},
	note = {Geoinformatica, Volume 12, Number 1 / March,},
	author = {T. Ceccarelli and D. Centeno and Fosca Giannotti and A. Massolo and Christine Parent and Alessandra Raffaet{\`a} and Chiara Renso and Stefano Spaccapietra and Franco Turini}
}
@conference {PedreschiRT08,
	title = {Discrimination-aware data mining},
	booktitle = {KDD},
	year = {2008},
	pages = {560-568},
	author = {Dino Pedreschi and Salvatore Ruggieri and Franco Turini}
}
@inbook {RTBKKM08,
	title = {Knowledge Discovery from Geographical Data},
	booktitle = {Mobility, Data Mining and Privacy},
	year = {2008},
	pages = {243-265},
	author = {S Rinzivillo and Franco Turini and Vania Bogorny and Christine K{\"o}rner and Bart Kuijpers and Michael May}
}
@conference {GiannottiPT08,
	title = {Mobility, Data Mining and Privacy the Experience of the GeoPKDD Project},
	booktitle = {PinKDD},
	year = {2008},
	pages = {25-32},
	author = {Fosca Giannotti and Dino Pedreschi and Franco Turini}
}
@conference {471,
	title = {Ontology-Based Business Plan Classification},
	booktitle = {Enterprise Distributed Object Computing Conference (EDOC)},
	year = {2008},
	month = {2008},
	isbn = {978-0-7695-3373-5},
	doi = {http://dx.doi.org/10.1109/EDOC.2008.30},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=4634789},
	author = {Franco Turini and Barbara Furletti and Miriam Baglioni and Laura Spinsanti and Andrea Bellandi}
}
@conference {BaglioniBFST08,
	title = {Ontology-Based Business Plan Classification},
	booktitle = {EDOC},
	year = {2008},
	pages = {365-371},
	author = {Miriam Baglioni and Andrea Bellandi and Barbara Furletti and Laura Spinsanti and Franco Turini}
}
@inbook {PedreschiBTVAMMS,
	title = {Privacy Protection: Regulations and Technologies, Opportunities and Threats},
	booktitle = {Mobility, Data Mining and Privacy},
	year = {2008},
	pages = {101-119},
	author = {Dino Pedreschi and Francesco Bonchi and Franco Turini and Vassilios S. Verykios and Maurizio Atzori and Bradley Malin and Bart Moelans and Y{\"u}cel Saygin}
}
@article {RT07,
	title = {Knowledge discovery from spatial transactions},
	journal = {Journal of Intelligent Information Systems},
	volume = {28},
	number = {1},
	year = {2007},
	pages = {1-22},
	author = {S Rinzivillo and Franco Turini}
}
@conference {DBLP:conf/bibm/BerlingerioBGT07,
	title = {Mining Clinical Data with a Temporal Dimension: A Case Study},
	booktitle = {BIBM},
	year = {2007},
	pages = {429-436},
	author = {Michele Berlingerio and Francesco Bonchi and Fosca Giannotti and Franco Turini}
}
@conference {DBLP:conf/icdm/BerlingerioBGT07,
	title = {Time-Annotated Sequences for Medical Data Mining},
	booktitle = {ICDM Workshops},
	year = {2007},
	pages = {133-138},
	author = {Michele Berlingerio and Francesco Bonchi and Fosca Giannotti and Franco Turini}
}
@conference {TuriniBFR06,
	title = {Examples of Integration of Induction and Deduction in Knowledge Discovery},
	booktitle = {Reasoning, Action and Interaction in AI Theories and Systems},
	year = {2006},
	pages = {307-326},
	author = {Franco Turini and Miriam Baglioni and Barbara Furletti and S Rinzivillo}
}
@inbook {466,
	title = {Examples of Integration of Induction and Deduction in Knowledge Discovery},
	booktitle = {Reasoning, Action and Interaction in AI Theories and Systems},
	series = {LNAI},
	volume = {4155},
	year = {2006},
	pages = {307-326},
	doi = {10.1007/11829263_17},
	url = {http://www.springerlink.com/content/m400v4507476n18g/fulltext.pdf},
	author = {Franco Turini and Miriam Baglioni and Barbara Furletti and S Rinzivillo}
}
@conference {465,
	title = {A Tool for Economic Plans analysis based on expert knowledge and data mining techniques},
	booktitle = { IADIS International Conference Applied Computing 2006 },
	year = {2006},
	month = {2006},
	isbn = {972-8924-09-7},
	url = {http://www.iadisportal.org/digital-library/mdownload/a-tool-for-economic-plans-analysis-based-on-expert-knowledge-and-data-mining-techniques},
	author = {Miriam Baglioni and Barbara Furletti and Franco Turini}
}
@conference {464,
	title = {DrC4.5: Improving C4.5 by means of Prior Knowledge},
	booktitle = {ACM Symposium on Applied Computing},
	year = {2005},
	publisher = {ACM},
	organization = {ACM},
	address = {Santa Fe, New Mexico, USA},
	isbn = {1-58113-964-0},
	doi = {http://dx.doi.org/10.1145/1066677.1066787},
	url = {http://dl.acm.org/ft_gateway.cfm?id=1066787\&ftid=311609\&dwn=1\&CFID=96873366\&CFTOKEN=59233511},
	author = {Miriam Baglioni and Barbara Furletti and Franco Turini}
}
@conference {RinzivilloT05,
	title = {Extracting spatial association rules from spatial transactions},
	booktitle = {ACM GIS},
	year = {2005},
	pages = {79-86},
	author = {S Rinzivillo and Franco Turini}
}
@conference {RinzivilloT04,
	title = {Classification in Geographical Information Systems},
	booktitle = {PKDD},
	year = {2004},
	pages = {374-385},
	author = {S Rinzivillo and Franco Turini}
}
@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}
}
@article {DBLP:journals/gis/MancarellaRRT04,
	title = {Integrating knowledge representation and reasoning in Geographical Information Systems},
	journal = {International Journal of Geographical Information Science},
	volume = {18},
	number = {4},
	year = {2004},
	pages = {417-447},
	author = {Paolo Mancarella and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@article {MRRT03,
	title = {Integrating Knowledge Representation and Reasoning in Geographical},
	year = {2004},
	note = {information systems. {\em International Journal of GIS,Vol 18 (4), June }.},
	author = {Paolo Mancarella and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@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}
}
@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}
}
@conference {DBLP:conf/cinq/GiannottiMT04,
	title = {Towards a Logic Query Language for Data Mining},
	booktitle = {Database Support for Data Mining Applications},
	year = {2004},
	pages = {76-94},
	author = {Fosca Giannotti and Giuseppe Manco and Franco Turini}
}
@conference {DBLP:conf/aiia/RaffaetaRT03,
	title = {Qualitative Spatial Reasoning in a Logical Framework},
	booktitle = {AI*IA},
	year = {2003},
	pages = {78-90},
	author = {Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/gis/RaffaetaTR02,
	title = {Enhancing GISs for spatio-temporal reasoning},
	booktitle = {ACM-GIS},
	year = {2002},
	pages = {42-48},
	author = {Alessandra Raffaet{\`a} and Franco Turini and Chiara Renso}
}
@conference {DBLP:conf/sebd/RaffaeteaRT02,
	title = {Qualitative Reasoning in a Spatio-Temporal Language},
	booktitle = {SEBD},
	year = {2002},
	pages = {105-118},
	author = {Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/sebd/GiannottiRRT01,
	title = {Complex Reasoning on Geographical Data},
	booktitle = {SEBD},
	year = {2001},
	pages = {331-338},
	author = {Fosca Giannotti and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/sebd/GiannottiRRT01,
	title = {Complex Reasoning on Geographical Data},
	booktitle = {SEBD},
	year = {2001},
	pages = {331-338},
	author = {Fosca Giannotti and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@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}
}
@article {DBLP:journals/jlp/AquilinoAFRT00,
	title = {Using MedLan to Integrate Geographical Data},
	journal = {J. Log. Program.},
	volume = {43},
	number = {1},
	year = {2000},
	pages = {3-14},
	author = {Domenico Aquilino and Patrizia Asirelli and A Formuso and Chiara Renso and Franco Turini}
}
@article {AAFRT99,
	title = {Using Medlan to Integrate Geographical Data},
	journal = {Journal of Logic Programming},
	year = {2000},
	note = {43(1):.},
	pages = {3{\textendash}14},
	author = {Domenico Aquilino and Patrizia Asirelli and A Formuso and Chiara Renso and Franco Turini}
}
@article {BRT99,
	title = {Dynamic Composition of Parameterised Logic Modules},
	journal = {Computer Languages},
	year = {1999},
	note = {25(4):.},
	pages = {211{\textendash}242},
	author = {Antonio Brogi and Chiara Renso and Franco Turini}
}
@article {DBLP:journals/cl/BrogiRT99,
	title = {Dynamic composition of parameterised logic modules},
	journal = {Comput. Lang.},
	volume = {25},
	number = {4},
	year = {1999},
	pages = {211-242},
	author = {Antonio Brogi and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/dmkd/GiannottiMPT99,
	title = {Experiences with a Logic-based knowledge discovery Support Environment},
	booktitle = {1999 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery},
	year = {1999},
	author = {Fosca Giannotti and Giuseppe Manco and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/aiia/GiannottiMPT99,
	title = {Experiences with a Logic-Based Knowledge Discovery Support Environment},
	booktitle = {AI*IA},
	year = {1999},
	pages = {202-213},
	author = {Fosca Giannotti and Giuseppe Manco and Dino Pedreschi and Franco Turini}
}
@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/tc11-5/AsirelliRT98,
	title = {The Constraint Operator of MedLan: Its Efficient Implementation and Use},
	booktitle = {IICIS},
	year = {1998},
	pages = {41-55},
	author = {Patrizia Asirelli and Chiara Renso and Franco Turini}
}
@article {AART97,
	title = {Applying Restriction Constraint to Deductive Databases},
	journal = {Annals of Mathematics and Artificial Intelligence},
	year = {1997},
	note = {1997},
	pages = {3{\textendash}25},
	author = {Domenico Aquilino and Patrizia Asirelli and Chiara Renso and Franco Turini}
}
@article {DBLP:journals/amai/AquilinoART97,
	title = {Applying Restriction Constraints to Deductive Databases},
	journal = {Ann. Math. Artif. Intell.},
	volume = {19},
	number = {1-2},
	year = {1997},
	pages = {3-25},
	author = {Domenico Aquilino and Patrizia Asirelli and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/lid/AsirelliRT96,
	title = {Language Extensions for Semantic Integration of Deductive Databases},
	booktitle = {Logic in Databases},
	year = {1996},
	pages = {415-434},
	author = {Patrizia Asirelli and Chiara Renso and Franco Turini}
}
@inbook {ART96,
	title = {Towards {D}eclarative {GIS} {A}nalysis},
	year = {1996},
	note = {{\em Proocedings of the {F}ourth {ACM} {W}orkshop on {A}dvances in {G}eographic {I}nformation {S}ystems}, pages.},
	pages = {99{\textendash}105},
	author = {Domenico Aquilino and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/gis/AquilinoRT96,
	title = {Towards Declarative GIS Analysis},
	booktitle = {ACM-GIS},
	year = {1996},
	pages = {98-104},
	author = {Domenico Aquilino and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/fapr/MontesiRT96,
	title = {Using Temporary Integrity Constraints to Optimize Databases},
	booktitle = {FAPR},
	year = {1996},
	pages = {430-435},
	author = {Danilo Montesi and Chiara Renso and Franco Turini}
}
@article {AART95,
	title = {An Operator for Composing Deductive Databases with Theories of Constraints},
	year = {1995},
	note = {Logic Programming and Nonmonotonic Reasoning, Third International Conference Lecture Notes in Computer Science vol 928,},
	pages = {57{\textendash}70},
	author = {Domenico Aquilino and Patrizia Asirelli and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/lpnmr/AquilinoART95,
	title = {An Operator for Composing Deductive Databases with Theories of Constraints},
	booktitle = {LPNMR},
	year = {1995},
	pages = {57-70},
	author = {Domenico Aquilino and Patrizia Asirelli and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/agp/BrogiRT94,
	title = {Amalgamating Language and Meta-language for Composing Logic Programs},
	booktitle = {GULP-PRODE (2)},
	year = {1994},
	pages = {408-422},
	author = {Antonio Brogi and Chiara Renso and Franco Turini}
}
@article {BCMMPRT94,
	title = {Implementations of Program Composition Operations},
	year = {1994},
	note = {Programming Language Implementation and Logic Programming Lecture Notes in Computer Science, volume 844,},
	pages = {292{\textendash}307},
	author = {Antonio Brogi and A. Chiarelli and Paolo Mancarella and V. Mazzotta and Dino Pedreschi and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/plilp/BrogiCMMPRT94,
	title = {Implementations of Program Composition Operations},
	booktitle = {PLILP},
	year = {1994},
	pages = {292-307},
	author = {Antonio Brogi and A. Chiarelli and Paolo Mancarella and V. Mazzotta and Dino Pedreschi and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/plilp/BrogiCMMPRT94,
	title = {Implementations of Program Composition Operations},
	booktitle = {PLILP},
	year = {1994},
	pages = {292-307},
	author = {Antonio Brogi and A. Chiarelli and Paolo Mancarella and V. Mazzotta and Dino Pedreschi and Chiara Renso and Franco Turini}
}
@article {DBLP:journals/toplas/BrogiMPT94,
	title = {Modular Logic Programming},
	journal = {ACM Trans. Program. Lang. Syst.},
	volume = {16},
	number = {4},
	year = {1994},
	pages = {1361-1398},
	author = {Antonio Brogi and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/meta/BrogiMPT92,
	title = {Meta for Modularising Logic Programming},
	booktitle = {META},
	year = {1992},
	pages = {105-119},
	author = {Antonio Brogi and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
@book {DBLP:books/mit/pfenning92/BertolinoMPT92,
	title = {The Type System of LML},
	series = {Types in Logic Programming},
	year = {1992},
	pages = {313-332},
	author = {Bruno Bertolino and Luigi Meo and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/iclp/BrogiMPT91,
	title = {Theory Construction in Computational Logic},
	booktitle = {ICLP Workshop on Construction of Logic Programs},
	year = {1991},
	pages = {241-250},
	author = {Antonio Brogi and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/plilp/BrogiMPT90,
	title = {Logic Programming within a Functional Framework},
	booktitle = {PLILP},
	year = {1990},
	pages = {372-386},
	author = {Antonio Brogi and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
@article {DBLP:journals/jlp/BarbutiMPT90,
	title = {A Transformational Approach to Negation in Logic Programming},
	journal = {J. Log. Program.},
	volume = {8},
	number = {3},
	year = {1990},
	pages = {201-228},
	author = {Roberto Barbuti and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/ecai/BrogiMPT90,
	title = {Universal Quantification by Case Analysis},
	booktitle = {ECAI},
	year = {1990},
	pages = {111-116},
	author = {Antonio Brogi and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/fgcs/BertolinoMMNPT88,
	title = {A Progress Report on the LML Project},
	booktitle = {FGCS},
	year = {1988},
	pages = {675-684},
	author = {Bruno Bertolino and Paolo Mancarella and Luigi Meo and Luca Nini and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/tapsoft/BarbutiMPT87,
	title = {Intensional Negation of Logic Programs: Examples and Implementation Techniques},
	booktitle = {TAPSOFT, Vol.2},
	year = {1987},
	pages = {96-110},
	author = {Roberto Barbuti and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
@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}
}
