TY - JOUR T1 - GLocalX - From Local to Global Explanations of Black Box AI Models Y1 - 2021 A1 - Mattia Setzu A1 - Riccardo Guidotti A1 - Anna Monreale A1 - Franco Turini A1 - Dino Pedreschi A1 - Fosca Giannotti AB - 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 “black boxes” 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 “local” explanations. We present GLocalX, a “local-first” 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. VL - 294 SN - 0004-3702 UR - https://www.sciencedirect.com/science/article/pii/S0004370221000084 JO - Artificial Intelligence ER - TY - JOUR T1 - Bias in data-driven artificial intelligence systems—An introductory survey JF - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery Y1 - 2020 A1 - Ntoutsi, Eirini A1 - Fafalios, Pavlos A1 - Gadiraju, Ujwal A1 - Iosifidis, Vasileios A1 - Nejdl, Wolfgang A1 - Vidal, Maria-Esther A1 - Salvatore Ruggieri A1 - Franco Turini A1 - Papadopoulos, Symeon A1 - Krasanakis, Emmanouil A1 - others AB - 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. VL - 10 UR - https://onlinelibrary.wiley.com/doi/full/10.1002/widm.1356 ER - TY - CONF T1 - Global Explanations with Local Scoring T2 - Machine Learning and Knowledge Discovery in Databases Y1 - 2020 A1 - Mattia Setzu A1 - Riccardo Guidotti A1 - Anna Monreale A1 - Franco Turini ED - Cellier, Peggy ED - Driessens, Kurt AB - Artificial Intelligence systems often adopt machine learning models encoding complex algorithms with potentially unknown behavior. As the application of these “black box” 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. JF - Machine Learning and Knowledge Discovery in Databases PB - Springer International Publishing CY - Cham SN - 978-3-030-43823-4 UR - https://link.springer.com/chapter/10.1007%2F978-3-030-43823-4_14 ER - TY - JOUR T1 - Factual and Counterfactual Explanations for Black Box Decision Making JF - IEEE Intelligent Systems Y1 - 2019 A1 - Riccardo Guidotti A1 - Anna Monreale A1 - Fosca Giannotti A1 - Dino Pedreschi A1 - Salvatore Ruggieri A1 - Franco Turini AB - 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. UR - https://ieeexplore.ieee.org/abstract/document/8920138 ER - TY - CONF T1 - Meaningful explanations of Black Box AI decision systems T2 - Proceedings of the AAAI Conference on Artificial Intelligence Y1 - 2019 A1 - Dino Pedreschi A1 - Fosca Giannotti A1 - Riccardo Guidotti A1 - Anna Monreale A1 - Salvatore Ruggieri A1 - Franco Turini AB - Black box AI systems for automated decision making, often based on machine learning over (big) data, map a user’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. JF - Proceedings of the AAAI Conference on Artificial Intelligence UR - https://aaai.org/ojs/index.php/AAAI/article/view/5050 ER - TY - RPRT T1 - Local Rule-Based Explanations of Black Box Decision Systems Y1 - 2018 A1 - Riccardo Guidotti A1 - Anna Monreale A1 - Salvatore Ruggieri A1 - Dino Pedreschi A1 - Franco Turini A1 - Fosca Giannotti JF - arXiv preprint arXiv:1805.10820 ER - TY - RPRT T1 - Open the Black Box Data-Driven Explanation of Black Box Decision Systems Y1 - 2018 A1 - Dino Pedreschi A1 - Fosca Giannotti A1 - Riccardo Guidotti A1 - Anna Monreale A1 - Luca Pappalardo A1 - Salvatore Ruggieri A1 - Franco Turini JF - arXiv preprint arXiv:1806.09936 ER - TY - JOUR T1 - A survey of methods for explaining black box models JF - ACM computing surveys (CSUR) Y1 - 2018 A1 - Riccardo Guidotti A1 - Anna Monreale A1 - Salvatore Ruggieri A1 - Franco Turini A1 - Fosca Giannotti A1 - Dino Pedreschi AB - 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. VL - 51 UR - https://dl.acm.org/doi/abs/10.1145/3236009 ER - TY - JOUR T1 - 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 JF - Education and Information Technologies Y1 - 2017 A1 - Mukala, Patrick A1 - Cerone, Antonio A1 - Franco Turini AB - 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. VL - 22 UR - https://link.springer.com/article/10.1007/s10639-017-9573-6 ER - TY - JOUR T1 - Survey on using constraints in data mining JF - Data Mining and Knowledge Discovery Y1 - 2017 A1 - Valerio Grossi A1 - Andrea Romei A1 - Franco Turini AB - 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. VL - 31 ER - TY - CONF T1 - Classification Rule Mining Supported by Ontology for Discrimination Discovery T2 - Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on Y1 - 2016 A1 - Luong, Binh Thanh A1 - Salvatore Ruggieri A1 - Franco Turini AB - 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. JF - Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on PB - IEEE ER - TY - CHAP T1 - Data Mining and Constraints: An Overview T2 - Data Mining and Constraint Programming Y1 - 2016 A1 - Valerio Grossi A1 - Dino Pedreschi A1 - Franco Turini AB - 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. JF - Data Mining and Constraint Programming PB - Springer International Publishing ER - TY - CONF T1 - A KDD process for discrimination discovery T2 - Joint European Conference on Machine Learning and Knowledge Discovery in Databases Y1 - 2016 A1 - Salvatore Ruggieri A1 - Franco Turini AB - 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. JF - Joint European Conference on Machine Learning and Knowledge Discovery in Databases PB - Springer International Publishing ER - TY - CONF T1 - Clustering Formulation Using Constraint Optimization T2 - Software Engineering and Formal Methods - {SEFM} 2015 Collocated Workshops: ATSE, HOFM, MoKMaSD, and VERY*SCART, York, UK, September 7-8, 2015, Revised Selected Papers Y1 - 2015 A1 - Valerio Grossi A1 - Anna Monreale A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Franco Turini AB - The problem of clustering a set of data is a textbook machine learning problem, but at the same time, at heart, a typical optimization problem. Given an objective function, such as minimizing the intra-cluster distances or maximizing the inter-cluster distances, the task is to find an assignment of data points to clusters that achieves this objective. In this paper, we present a constraint programming model for a centroid based clustering and one for a density based clustering. In particular, as a key contribution, we show how the expressivity introduced by the formulation of the problem by constraint programming makes the standard problem easy to be extended with other constraints that permit to generate interesting variants of the problem. We show this important aspect in two different ways: first, we show how the formulation of the density-based clustering by constraint programming makes it very similar to the label propagation problem and then, we propose a variant of the standard label propagation approach. JF - Software Engineering and Formal Methods - {SEFM} 2015 Collocated Workshops: ATSE, HOFM, MoKMaSD, and VERY*SCART, York, UK, September 7-8, 2015, Revised Selected Papers PB - Springer Berlin Heidelberg UR - http://dx.doi.org/10.1007/978-3-662-49224-6_9 ER - TY - ABST T1 - An exploration of learning processes as process maps in FLOSS repositories Y1 - 2015 A1 - Mukala, Patrick A1 - Cerone, Antonio A1 - Franco Turini AB - 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’ 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. UR - http://eprints.adm.unipi.it/id/eprint/2344 ER - TY - CONF T1 - The layered structure of company share networks T2 - Data Science and Advanced Analytics (DSAA), 2015. 36678 2015. IEEE International Conference on Y1 - 2015 A1 - Andrea Romei A1 - Salvatore Ruggieri A1 - Franco Turini AB - 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× the largest network studied so far. JF - Data Science and Advanced Analytics (DSAA), 2015. 36678 2015. IEEE International Conference on PB - IEEE ER - TY - CONF T1 - Mining learning processes from FLOSS mailing archives T2 - Conference on e-Business, e-Services and e-Society Y1 - 2015 A1 - Mukala, Patrick A1 - Cerone, Antonio A1 - Franco Turini AB - 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’ interaction and activities, we analyze participants’ 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. JF - Conference on e-Business, e-Services and e-Society PB - Springer, Cham ER - TY - CONF T1 - An abstract state machine (ASM) representation of learning process in FLOSS communities T2 - International Conference on Software Engineering and Formal Methods Y1 - 2014 A1 - Mukala, Patrick A1 - Cerone, Antonio A1 - Franco Turini AB - 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. JF - International Conference on Software Engineering and Formal Methods PB - Springer, Cham ER - TY - CONF T1 - Ontolifloss: Ontology for learning processes in FLOSS communities T2 - International Conference on Software Engineering and Formal Methods Y1 - 2014 A1 - Mukala, Patrick A1 - Cerone, Antonio A1 - Franco Turini AB - 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. JF - International Conference on Software Engineering and Formal Methods PB - Springer, Cham ER - TY - CONF T1 - Process mining event logs from FLOSS data: state of the art and perspectives T2 - International Conference on Software Engineering and Formal Methods Y1 - 2014 A1 - Mukala, Patrick A1 - Cerone, Antonio A1 - Franco Turini AB - 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. JF - International Conference on Software Engineering and Formal Methods PB - Springer, Cham ER - TY - CHAP T1 - The discovery of discrimination T2 - Discrimination and privacy in the information society Y1 - 2013 A1 - Dino Pedreschi A1 - Salvatore Ruggieri A1 - Franco Turini JF - Discrimination and privacy in the information society PB - Springer ER - TY - JOUR T1 - Discrimination discovery in scientific project evaluation: A case study JF - Expert Systems with Applications Y1 - 2013 A1 - Andrea Romei A1 - Salvatore Ruggieri A1 - Franco Turini VL - 40 ER - TY - JOUR T1 - Spatio-Temporal Data JF - Spatio-Temporal Databases: Flexible Querying and Reasoning Y1 - 2013 A1 - Mirco Nanni A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Franco Turini ER - TY - JOUR T1 - Knowledge Discovery in Ontologies JF - Intelligent Data Analysis Y1 - 2012 A1 - Barbara Furletti A1 - Franco Turini VL - 16 UR - http://iospress.metapress.com/content/765h53w41286p578/fulltext.pdf ER - TY - CHAP T1 - What else can be extracted from ontologies? Influence Rules T2 - Software and Data Technologies Y1 - 2012 A1 - Franco Turini A1 - Barbara Furletti JF - Software and Data Technologies T3 - Communications in Computer and Information Science PB - Springer ER - TY - CONF T1 - Mining Influence Rules out of Ontologies T2 - International Conference on Software and Data Technologies (ICSOFT) Y1 - 2011 A1 - Barbara Furletti A1 - Franco Turini JF - International Conference on Software and Data Technologies (ICSOFT) CY - Siviglia, Spagna ER - TY - JOUR T1 - Improving the Business Plan Evaluation Process: the Role of Intangibles JF - Quality Technology & Quantitative Management Y1 - 2010 A1 - Barbara Furletti A1 - Franco Turini A1 - Andrea Bellandi A1 - Miriam Baglioni A1 - Chiara Pratesi VL - 7 UR - http://web.it.nctu.edu.tw/~qtqm/upcomingpapers/2010V7N1/2010V7N1_F3.pdf ER - TY - CONF T1 - Integrating induction and deduction for finding evidence of discrimination T2 - ICAIL Y1 - 2009 A1 - Dino Pedreschi A1 - Salvatore Ruggieri A1 - Franco Turini JF - ICAIL ER - TY - CONF T1 - Measuring Discrimination in Socially-Sensitive Decision Records T2 - SDM Y1 - 2009 A1 - Dino Pedreschi A1 - Salvatore Ruggieri A1 - Franco Turini JF - SDM ER - TY - CHAP T1 - Mining Clinical, Immunological, and Genetic Data of Solid Organ Transplantation T2 - Biomedical Data and Applications Y1 - 2009 A1 - Michele Berlingerio A1 - Francesco Bonchi A1 - Michele Curcio A1 - Fosca Giannotti A1 - Franco Turini JF - Biomedical Data and Applications ER - TY - JOUR T1 - An Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology JF - GeoInformatica Y1 - 2008 A1 - Alessandra Raffaetà A1 - T. Ceccarelli A1 - D. Centeno A1 - Fosca Giannotti A1 - A. Massolo A1 - Christine Parent A1 - Chiara Renso A1 - Stefano Spaccapietra A1 - Franco Turini VL - 12 ER - TY - JOUR T1 - An Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology Y1 - 2008 A1 - T. Ceccarelli A1 - D. Centeno A1 - Fosca Giannotti A1 - A. Massolo A1 - Christine Parent A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Stefano Spaccapietra A1 - Franco Turini N1 - Geoinformatica, Volume 12, Number 1 / March, ER - TY - CONF T1 - Discrimination-aware data mining T2 - KDD Y1 - 2008 A1 - Dino Pedreschi A1 - Salvatore Ruggieri A1 - Franco Turini JF - KDD ER - TY - CHAP T1 - Knowledge Discovery from Geographical Data T2 - Mobility, Data Mining and Privacy Y1 - 2008 A1 - S Rinzivillo A1 - Franco Turini A1 - Vania Bogorny A1 - Christine Körner A1 - Bart Kuijpers A1 - Michael May JF - Mobility, Data Mining and Privacy ER - TY - CONF T1 - Mobility, Data Mining and Privacy the Experience of the GeoPKDD Project T2 - PinKDD Y1 - 2008 A1 - Fosca Giannotti A1 - Dino Pedreschi A1 - Franco Turini JF - PinKDD ER - TY - CONF T1 - Ontology-Based Business Plan Classification T2 - EDOC Y1 - 2008 A1 - Miriam Baglioni A1 - Andrea Bellandi A1 - Barbara Furletti A1 - Laura Spinsanti A1 - Franco Turini JF - EDOC ER - TY - CONF T1 - Ontology-Based Business Plan Classification T2 - Enterprise Distributed Object Computing Conference (EDOC) Y1 - 2008 A1 - Franco Turini A1 - Barbara Furletti A1 - Miriam Baglioni A1 - Laura Spinsanti A1 - Andrea Bellandi JF - Enterprise Distributed Object Computing Conference (EDOC) SN - 978-0-7695-3373-5 UR - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4634789 ER - TY - CHAP T1 - Privacy Protection: Regulations and Technologies, Opportunities and Threats T2 - Mobility, Data Mining and Privacy Y1 - 2008 A1 - Dino Pedreschi A1 - Francesco Bonchi A1 - Franco Turini A1 - Vassilios S. Verykios A1 - Maurizio Atzori A1 - Bradley Malin A1 - Bart Moelans A1 - Yücel Saygin JF - Mobility, Data Mining and Privacy ER - TY - JOUR T1 - Knowledge discovery from spatial transactions JF - Journal of Intelligent Information Systems Y1 - 2007 A1 - S Rinzivillo A1 - Franco Turini VL - 28 ER - TY - CONF T1 - Mining Clinical Data with a Temporal Dimension: A Case Study T2 - BIBM Y1 - 2007 A1 - Michele Berlingerio A1 - Francesco Bonchi A1 - Fosca Giannotti A1 - Franco Turini JF - BIBM ER - TY - CONF T1 - Time-Annotated Sequences for Medical Data Mining T2 - ICDM Workshops Y1 - 2007 A1 - Michele Berlingerio A1 - Francesco Bonchi A1 - Fosca Giannotti A1 - Franco Turini JF - ICDM Workshops ER - TY - CONF T1 - Examples of Integration of Induction and Deduction in Knowledge Discovery T2 - Reasoning, Action and Interaction in AI Theories and Systems Y1 - 2006 A1 - Franco Turini A1 - Miriam Baglioni A1 - Barbara Furletti A1 - S Rinzivillo JF - Reasoning, Action and Interaction in AI Theories and Systems ER - TY - CHAP T1 - Examples of Integration of Induction and Deduction in Knowledge Discovery T2 - Reasoning, Action and Interaction in AI Theories and Systems Y1 - 2006 A1 - Franco Turini A1 - Miriam Baglioni A1 - Barbara Furletti A1 - S Rinzivillo JF - Reasoning, Action and Interaction in AI Theories and Systems T3 - LNAI VL - 4155 UR - http://www.springerlink.com/content/m400v4507476n18g/fulltext.pdf ER - TY - CONF T1 - A Tool for Economic Plans analysis based on expert knowledge and data mining techniques T2 - IADIS International Conference Applied Computing 2006 Y1 - 2006 A1 - Miriam Baglioni A1 - Barbara Furletti A1 - Franco Turini JF - IADIS International Conference Applied Computing 2006 SN - 972-8924-09-7 UR - http://www.iadisportal.org/digital-library/mdownload/a-tool-for-economic-plans-analysis-based-on-expert-knowledge-and-data-mining-techniques ER - TY - CONF T1 - DrC4.5: Improving C4.5 by means of Prior Knowledge T2 - ACM Symposium on Applied Computing Y1 - 2005 A1 - Miriam Baglioni A1 - Barbara Furletti A1 - Franco Turini JF - ACM Symposium on Applied Computing PB - ACM CY - Santa Fe, New Mexico, USA SN - 1-58113-964-0 UR - http://dl.acm.org/ft_gateway.cfm?id=1066787&ftid=311609&dwn=1&CFID=96873366&CFTOKEN=59233511 ER - TY - CONF T1 - Extracting spatial association rules from spatial transactions T2 - ACM GIS Y1 - 2005 A1 - S Rinzivillo A1 - Franco Turini JF - ACM GIS ER - TY - CONF T1 - Classification in Geographical Information Systems T2 - PKDD Y1 - 2004 A1 - S Rinzivillo A1 - Franco Turini JF - PKDD ER - TY - CONF T1 - Deductive and Inductive Reasoning on Spatio-Temporal Data T2 - INAP/WLP Y1 - 2004 A1 - Mirco Nanni A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Franco Turini JF - INAP/WLP ER - TY - CONF T1 - Deductive and Inductive Reasoning on Trajectories T2 - SEBD Y1 - 2004 A1 - Mirco Nanni A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Franco Turini JF - SEBD ER - TY - JOUR T1 - Integrating Knowledge Representation and Reasoning in Geographical Y1 - 2004 A1 - Paolo Mancarella A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Franco Turini N1 - information systems. {\em International Journal of GIS,Vol 18 (4), June }. ER - TY - JOUR T1 - Integrating knowledge representation and reasoning in Geographical Information Systems JF - International Journal of Geographical Information Science Y1 - 2004 A1 - Paolo Mancarella A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Franco Turini VL - 18 ER - TY - JOUR T1 - \newblock{A Declarative Framework for Reasoning on Spatio-temporal Data} Y1 - 2004 A1 - Mirco Nanni A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Franco Turini N1 - \newblock{Book chapter in Spatio-temporal databases, flexible querying and reasoning, R. de Caluwe, G. de Trè, G. Bordogna editors, Physica Verlag }. ER - TY - JOUR T1 - Specifying Mining Algorithms with Iterative User-Defined Aggregates JF - IEEE Trans. Knowl. Data Eng. Y1 - 2004 A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Franco Turini VL - 16 ER - TY - CONF T1 - Towards a Logic Query Language for Data Mining T2 - Database Support for Data Mining Applications Y1 - 2004 A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Franco Turini JF - Database Support for Data Mining Applications ER - TY - CONF T1 - Qualitative Spatial Reasoning in a Logical Framework T2 - AI*IA Y1 - 2003 A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Franco Turini JF - AI*IA ER - TY - CONF T1 - Enhancing GISs for spatio-temporal reasoning T2 - ACM-GIS Y1 - 2002 A1 - Alessandra Raffaetà A1 - Franco Turini A1 - Chiara Renso JF - ACM-GIS ER - TY - CONF T1 - Qualitative Reasoning in a Spatio-Temporal Language T2 - SEBD Y1 - 2002 A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Franco Turini JF - SEBD ER - TY - CONF T1 - Complex Reasoning on Geographical Data T2 - SEBD Y1 - 2001 A1 - Fosca Giannotti A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Franco Turini JF - SEBD ER - TY - CONF T1 - Complex Reasoning on Geographical Data T2 - SEBD Y1 - 2001 A1 - Fosca Giannotti A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Franco Turini JF - SEBD ER - TY - CONF T1 - Specifying Mining Algorithms with Iterative User-Defined Aggregates: A Case Study T2 - PKDD Y1 - 2001 A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Franco Turini JF - PKDD ER - TY - JOUR T1 - Using Medlan to Integrate Geographical Data JF - Journal of Logic Programming Y1 - 2000 A1 - Domenico Aquilino A1 - Patrizia Asirelli A1 - A Formuso A1 - Chiara Renso A1 - Franco Turini N1 - 43(1):. ER - TY - JOUR T1 - Using MedLan to Integrate Geographical Data JF - J. Log. Program. Y1 - 2000 A1 - Domenico Aquilino A1 - Patrizia Asirelli A1 - A Formuso A1 - Chiara Renso A1 - Franco Turini VL - 43 ER - TY - JOUR T1 - Dynamic Composition of Parameterised Logic Modules JF - Computer Languages Y1 - 1999 A1 - Antonio Brogi A1 - Chiara Renso A1 - Franco Turini N1 - 25(4):. ER - TY - JOUR T1 - Dynamic composition of parameterised logic modules JF - Comput. Lang. Y1 - 1999 A1 - Antonio Brogi A1 - Chiara Renso A1 - Franco Turini VL - 25 ER - TY - CONF T1 - Experiences with a Logic-based knowledge discovery Support Environment T2 - 1999 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery Y1 - 1999 A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Dino Pedreschi A1 - Franco Turini JF - 1999 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery ER - TY - CONF T1 - Experiences with a Logic-Based Knowledge Discovery Support Environment T2 - AI*IA Y1 - 1999 A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Dino Pedreschi A1 - Franco Turini JF - AI*IA ER - TY - CONF T1 - Integration of Deduction and Induction for Mining Supermarket Sales Data T2 - SEBD Y1 - 1999 A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Franco Turini JF - SEBD ER - TY - CONF T1 - The Constraint Operator of MedLan: Its Efficient Implementation and Use T2 - IICIS Y1 - 1998 A1 - Patrizia Asirelli A1 - Chiara Renso A1 - Franco Turini JF - IICIS ER - TY - JOUR T1 - Applying Restriction Constraint to Deductive Databases JF - Annals of Mathematics and Artificial Intelligence Y1 - 1997 A1 - Domenico Aquilino A1 - Patrizia Asirelli A1 - Chiara Renso A1 - Franco Turini N1 - 1997 ER - TY - JOUR T1 - Applying Restriction Constraints to Deductive Databases JF - Ann. Math. Artif. Intell. Y1 - 1997 A1 - Domenico Aquilino A1 - Patrizia Asirelli A1 - Chiara Renso A1 - Franco Turini VL - 19 ER - TY - CONF T1 - Language Extensions for Semantic Integration of Deductive Databases T2 - Logic in Databases Y1 - 1996 A1 - Patrizia Asirelli A1 - Chiara Renso A1 - Franco Turini JF - Logic in Databases ER - TY - CHAP T1 - Towards {D}eclarative {GIS} {A}nalysis Y1 - 1996 A1 - Domenico Aquilino A1 - Chiara Renso A1 - Franco Turini N1 - {\em Proocedings of the {F}ourth {ACM} {W}orkshop on {A}dvances in {G}eographic {I}nformation {S}ystems}, pages. ER - TY - CONF T1 - Towards Declarative GIS Analysis T2 - ACM-GIS Y1 - 1996 A1 - Domenico Aquilino A1 - Chiara Renso A1 - Franco Turini JF - ACM-GIS ER - TY - CONF T1 - Using Temporary Integrity Constraints to Optimize Databases T2 - FAPR Y1 - 1996 A1 - Danilo Montesi A1 - Chiara Renso A1 - Franco Turini JF - FAPR ER - TY - JOUR T1 - An Operator for Composing Deductive Databases with Theories of Constraints Y1 - 1995 A1 - Domenico Aquilino A1 - Patrizia Asirelli A1 - Chiara Renso A1 - Franco Turini N1 - Logic Programming and Nonmonotonic Reasoning, Third International Conference Lecture Notes in Computer Science vol 928, ER - TY - CONF T1 - An Operator for Composing Deductive Databases with Theories of Constraints T2 - LPNMR Y1 - 1995 A1 - Domenico Aquilino A1 - Patrizia Asirelli A1 - Chiara Renso A1 - Franco Turini JF - LPNMR ER - TY - CONF T1 - Amalgamating Language and Meta-language for Composing Logic Programs T2 - GULP-PRODE (2) Y1 - 1994 A1 - Antonio Brogi A1 - Chiara Renso A1 - Franco Turini JF - GULP-PRODE (2) ER - TY - JOUR T1 - Implementations of Program Composition Operations Y1 - 1994 A1 - Antonio Brogi A1 - A. Chiarelli A1 - Paolo Mancarella A1 - V. Mazzotta A1 - Dino Pedreschi A1 - Chiara Renso A1 - Franco Turini N1 - Programming Language Implementation and Logic Programming Lecture Notes in Computer Science, volume 844, ER - TY - CONF T1 - Implementations of Program Composition Operations T2 - PLILP Y1 - 1994 A1 - Antonio Brogi A1 - A. Chiarelli A1 - Paolo Mancarella A1 - V. Mazzotta A1 - Dino Pedreschi A1 - Chiara Renso A1 - Franco Turini JF - PLILP ER - TY - CONF T1 - Implementations of Program Composition Operations T2 - PLILP Y1 - 1994 A1 - Antonio Brogi A1 - A. Chiarelli A1 - Paolo Mancarella A1 - V. Mazzotta A1 - Dino Pedreschi A1 - Chiara Renso A1 - Franco Turini JF - PLILP ER - TY - JOUR T1 - Modular Logic Programming JF - ACM Trans. Program. Lang. Syst. Y1 - 1994 A1 - Antonio Brogi A1 - Paolo Mancarella A1 - Dino Pedreschi A1 - Franco Turini VL - 16 ER - TY - CONF T1 - Meta for Modularising Logic Programming T2 - META Y1 - 1992 A1 - Antonio Brogi A1 - Paolo Mancarella A1 - Dino Pedreschi A1 - Franco Turini JF - META ER - TY - BOOK T1 - The Type System of LML T2 - Types in Logic Programming Y1 - 1992 A1 - Bruno Bertolino A1 - Luigi Meo A1 - Dino Pedreschi A1 - Franco Turini JF - Types in Logic Programming ER - TY - CONF T1 - Theory Construction in Computational Logic T2 - ICLP Workshop on Construction of Logic Programs Y1 - 1991 A1 - Antonio Brogi A1 - Paolo Mancarella A1 - Dino Pedreschi A1 - Franco Turini JF - ICLP Workshop on Construction of Logic Programs ER - TY - CONF T1 - Logic Programming within a Functional Framework T2 - PLILP Y1 - 1990 A1 - Antonio Brogi A1 - Paolo Mancarella A1 - Dino Pedreschi A1 - Franco Turini JF - PLILP ER - TY - JOUR T1 - A Transformational Approach to Negation in Logic Programming JF - J. Log. Program. Y1 - 1990 A1 - Roberto Barbuti A1 - Paolo Mancarella A1 - Dino Pedreschi A1 - Franco Turini VL - 8 ER - TY - CONF T1 - Universal Quantification by Case Analysis T2 - ECAI Y1 - 1990 A1 - Antonio Brogi A1 - Paolo Mancarella A1 - Dino Pedreschi A1 - Franco Turini JF - ECAI ER - TY - CONF T1 - A Progress Report on the LML Project T2 - FGCS Y1 - 1988 A1 - Bruno Bertolino A1 - Paolo Mancarella A1 - Luigi Meo A1 - Luca Nini A1 - Dino Pedreschi A1 - Franco Turini JF - FGCS ER - TY - CONF T1 - Intensional Negation of Logic Programs: Examples and Implementation Techniques T2 - TAPSOFT, Vol.2 Y1 - 1987 A1 - Roberto Barbuti A1 - Paolo Mancarella A1 - Dino Pedreschi A1 - Franco Turini JF - TAPSOFT, Vol.2 ER - TY - JOUR T1 - Symbolic Evaluation with Structural Recursive Symbolic Constants JF - Sci. Comput. Program. Y1 - 1987 A1 - Fosca Giannotti A1 - Attilio Matteucci A1 - Dino Pedreschi A1 - Franco Turini VL - 9 ER - TY - JOUR T1 - Symbolic Semantics and Program Reduction JF - IEEE Trans. Software Eng. Y1 - 1985 A1 - Vincenzo Ambriola A1 - Fosca Giannotti A1 - Dino Pedreschi A1 - Franco Turini VL - 11 ER -