01264nas a2200109 4500008004100000245005200041210005200093520088000145100002301025700002401048856008201072 2018 eng d00aAssessing the Stability of Interpretable Models0 aAssessing the Stability of Interpretable Models3 aInterpretable 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, which, in particular, comprises data collection and filtering. Selection bias in data collection or in data pre-processing may affect the model learned. Although model induction algorithms are designed to learn to generalize, they pursue optimization of predictive accuracy. It remains unclear how interpretability is instead impacted. We conduct an experimental analysis to investigate whether interpretable models are able to cope with data selection bias as far as interpretability is concerned.1 aGuidotti, Riccardo1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/publications/assessing-stability-interpretable-models01425nas a2200337 4500008004100000245009100041210006900132260003800201300001400239520041400253100002000667700002200687700001600709700001300725700001400738700001500752700002100767700001900788700001700807700001400824700002100838700002000859700002300879700001500902700001800917700002100935700002400956700002500980700001501005856006701020 2018 eng d00aHow Data Mining and Machine Learning Evolved from Relational Data Base to Data Science0 aHow Data Mining and Machine Learning Evolved from Relational Dat bSpringer International Publishing a287–3063 aDuring the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.1 aAmato, Giuseppe1 aCandela, Leonardo1 aCastelli, D1 aEsuli, A1 aFalchi, F1 aGennaro, C1 aGiannotti, Fosca1 aMonreale, Anna1 aNanni, Mirco1 aPagano, P1 aPappalardo, Luca1 aPedreschi, Dino1 aPratesi, Francesca1 aRabitti, F1 aRinzivillo, S1 aRossetti, Giulio1 aRuggieri, Salvatore1 aSebastiani, Fabrizio1 aTesconi, M uhttps://link.springer.com/chapter/10.1007/978-3-319-61893-7_1700538nas a2200145 4500008004100000245006400041210006300105100002300168700001900191700002400210700002000234700001900254700002100273856009800294 2018 eng d00aLocal Rule-Based Explanations of Black Box Decision Systems0 aLocal RuleBased Explanations of Black Box Decision Systems1 aGuidotti, Riccardo1 aMonreale, Anna1 aRuggieri, Salvatore1 aPedreschi, Dino1 aTurini, Franco1 aGiannotti, Fosca uhttps://kdd.isti.cnr.it/publications/local-rule-based-explanations-black-box-decision-systems00599nas a2200157 4500008004100000245007700041210006900118100002000187700002100207700002300228700001900251700002100270700002400291700001900315856010700334 2018 eng d00aOpen the Black Box Data-Driven Explanation of Black Box Decision Systems0 aOpen the Black Box DataDriven Explanation of Black Box Decision 1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aMonreale, Anna1 aPappalardo, Luca1 aRuggieri, Salvatore1 aTurini, Franco uhttps://kdd.isti.cnr.it/publications/open-black-box-data-driven-explanation-black-box-decision-systems01720nas a2200181 4500008004100000245005600041210005400097300000700151490000700158520116300165100002301328700001901351700002401370700001901394700002101413700002001434856008401454 2018 eng d00aA survey of methods for explaining black box models0 asurvey of methods for explaining black box models a930 v513 aIn 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.1 aGuidotti, Riccardo1 aMonreale, Anna1 aRuggieri, Salvatore1 aTurini, Franco1 aGiannotti, Fosca1 aPedreschi, Dino uhttps://kdd.isti.cnr.it/publications/survey-methods-explaining-black-box-models01602nas a2200133 4500008004100000245005600041210005600097520113100153100002301284700001901307700002501326700002401351856009301375 2017 eng d00aEfficiently Clustering Very Large Attributed Graphs0 aEfficiently Clustering Very Large Attributed Graphs3 aAttributed graphs model real networks by enriching their nodes with attributes accounting for properties. Several techniques have been proposed for partitioning these graphs into clusters that are homogeneous with respect to both semantic attributes and to the structure of the graph. However, time and space complexities of state of the art algorithms limit their scalability to medium-sized graphs. We propose SToC (for Semantic-Topological Clustering), a fast and scalable algorithm for partitioning large attributed graphs. The approach is robust, being compatible both with categorical and with quantitative attributes, and it is tailorable, allowing the user to weight the semantic and topological components. Further, the approach does not require the user to guess in advance the number of clusters. SToC relies on well known approximation techniques such as bottom-k sketches, traditional graph-theoretic concepts, and a new perspective on the composition of heterogeneous distance measures. Experimental results demonstrate its ability to efficiently compute high-quality partitions of large scale attributed graphs.1 aBaroni, Alessandro1 aConte, Alessio1 aPatrignani, Maurizio1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/publications/efficiently-clustering-very-large-attributed-graphs00831nas a2200097 4500008004100000245004000041210004000081520053400121100002400655856005400679 2017 eng d00aEnumerating Distinct Decision Trees0 aEnumerating Distinct Decision Trees3 aThe search space for the feature selection problem in decision tree learning is the lattice of subsets of the available features. We provide an exact enumeration procedure of the subsets that lead to all and only the distinct decision trees. The procedure can be adopted to prune the search space of complete and heuristics search methods in wrapper models for feature selection. Based on this, we design a computational optimization of the sequential backward elimination heuristics with a performance improvement of up to 100X.1 aRuggieri, Salvatore uhttp://proceedings.mlr.press/v70/ruggieri17a.html01350nas a2200133 4500008004100000022001400041245005900055210005900114260000800173520094200181100002301123700002401146856004601170 2017 eng d a1573-767500aSegregation discovery in a social network of companies0 aSegregation discovery in a social network of companies cSep3 aWe 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.1 aBaroni, Alessandro1 aRuggieri, Salvatore uhttps://doi.org/10.1007/s10844-017-0485-001199nas a2200289 4500008004100000022004100041050001400082245004600096210004500142260001200187300000800199490000600207520037500213100002300588700002200611700002300633700002200656700002100678700002000699700002400719700001900743700002000762700002000782700002100802700002400823856006200847 2016 eng d aprint: 2095-8099 / online: 2096-0026 a10-1244/N00aBig Data Research in Italy: A Perspective0 aBig Data Research in Italy A Perspective c06/2016 a1630 v23 aThe aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains.1 aBergamaschi, Sonia1 aCarlini, Emanuele1 aCeci, Michelangelo1 aFurletti, Barbara1 aGiannotti, Fosca1 aMalerba, Donato1 aMezzanzanica, Mario1 aMonreale, Anna1 aPasi, Gabriella1 aPedreschi, Dino1 aPerego, Raffaele1 aRuggieri, Salvatore uhttp://engineering.org.cn/EN/abstract/article_12288.shtml01704nas a2200133 4500008004100000245007000041210006900111520127400180100001901454700002001473700001601493700002401509856003701533 2016 eng d00aCausal Discrimination Discovery Through Propensity Score Analysis0 aCausal Discrimination Discovery Through Propensity Score Analysi3 aSocial discrimination is considered illegal and unethical in the modern world. Such discrimination is often implicit in observed decisions' datasets, and anti-discrimination organizations seek to discover cases of discrimination and to understand the reasons behind them. Previous work in this direction adopted simple observational data analysis; however, this can produce biased results due to the effect of confounding variables. In this paper, we propose a causal discrimination discovery and understanding approach based on propensity score analysis. The propensity score is an effective statistical tool for filtering out the effect of confounding variables. We employ propensity score weighting to balance the distribution of individuals from protected and unprotected groups w.r.t. the confounding variables. For each individual in the dataset, we quantify its causal discrimination or favoritism with a neighborhood-based measure calculated on the balanced distributions. Subsequently, the causal discrimination/favoritism patterns are understood by learning a regression tree. Our approach avoids common pitfalls in observational data analysis and make its results legally admissible. We demonstrate the results of our approach on two discrimination datasets.1 aQureshi, Bilal1 aKamiran, Faisal1 aKarim, Asim1 aRuggieri, Salvatore uhttps://arxiv.org/abs/1608.0373501430nas a2200133 4500008004100000245008200041210006900123260000900192520091700201100002301118700002401141700001901165856011201184 2016 eng d00aClassification Rule Mining Supported by Ontology for Discrimination Discovery0 aClassification Rule Mining Supported by Ontology for Discriminat bIEEE3 aDiscrimination 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.1 aLuong, Binh, Thanh1 aRuggieri, Salvatore1 aTurini, Franco uhttps://kdd.isti.cnr.it/publications/classification-rule-mining-supported-ontology-discrimination-discovery00753nas a2200121 4500008004100000245004700041210004500088260003800133520033900171100002400510700001900534856007800553 2016 eng d00aA KDD process for discrimination discovery0 aKDD process for discrimination discovery bSpringer International Publishing3 aThe 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.1 aRuggieri, Salvatore1 aTurini, Franco uhttps://kdd.isti.cnr.it/publications/kdd-process-discrimination-discovery00463nas a2200109 4500008004100000245008900041210006900130300001200199490000600211100002400217856011200241 2015 eng d00aIntroduction to the special issue on Artificial Intelligence for Society and Economy0 aIntroduction to the special issue on Artificial Intelligence for a23–230 v91 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/publications/introduction-special-issue-artificial-intelligence-society-and-economy01378nas a2200133 4500008004100000245005200041210004800093260000900141520095100150100001801101700002401119700001901143856008201162 2015 eng d00aThe layered structure of company share networks0 alayered structure of company share networks bIEEE3 aWe 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.1 aRomei, Andrea1 aRuggieri, Salvatore1 aTurini, Franco uhttps://kdd.isti.cnr.it/publications/layered-structure-company-share-networks01127nas a2200121 4500008004100000245005900041210005900100260001900159520069200178100002300870700002400893856008800917 2015 eng d00aSegregation Discovery in a Social Network of Companies0 aSegregation Discovery in a Social Network of Companies bSpringer, Cham3 aWe 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.1 aBaroni, Alessandro1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/publications/segregation-discovery-social-network-companies01456nas a2200145 4500008004100000245006500041210006400106260003300170520092200203100002401125700001701149700002001166700002201186856010201208 2014 eng d00aAnti-discrimination analysis using privacy attack strategies0 aAntidiscrimination analysis using privacy attack strategies bSpringer, Berlin, Heidelberg3 aSocial discrimination discovery from data is an important task to identify illegal and unethical discriminatory patterns towards protected-by-law groups, e.g., ethnic minorities. We deploy privacy attack strategies as tools for discrimination discovery under hard assumptions which have rarely tackled in the literature: indirect discrimination discovery, privacy-aware discrimination discovery, and discrimination data recovery. The intuition comes from the intriguing parallel between the role of the anti-discrimination authority in the three scenarios above and the role of an attacker in private data publishing. We design strategies and algorithms inspired/based on Frèchet bounds attacks, attribute inference attacks, and minimality attacks to the purpose of unveiling hidden discriminatory practices. Experimental results show that they can be effective tools in the hands of anti-discrimination authorities.1 aRuggieri, Salvatore1 aHajian, Sara1 aKamiran, Faisal1 aZhang, Xiangliang uhttps://kdd.isti.cnr.it/publications/anti-discrimination-analysis-using-privacy-attack-strategies01216nas a2200157 4500008004100000245005100041210004400092300001400136490000800150520073400158100002400892700002200916700001700938700002500955856007800980 2014 eng d00aOn the complexity of quantified linear systems0 acomplexity of quantified linear systems a128–1340 v5183 aIn this paper, we explore the computational complexity of the conjunctive fragment of the first-order theory of linear arithmetic. Quantified propositional formulas of linear inequalities with (k−1) quantifier alternations are log-space complete in ΣkP or ΠkP depending on the initial quantifier. We show that when we restrict ourselves to quantified conjunctions of linear inequalities, i.e., quantified linear systems, the complexity classes collapse to polynomial time. In other words, the presence of universal quantifiers does not alter the complexity of the linear programming problem, which is known to be in P. Our result reinforces the importance of sentence formats from the perspective of computational complexity.1 aRuggieri, Salvatore1 aEirinakis, Pavlos1 aSubramani, K1 aWojciechowski, Piotr uhttps://kdd.isti.cnr.it/publications/complexity-quantified-linear-systems01257nas a2200145 4500008004100000245005600041210005500097300001400152490000700166520078100173100002100954700002400975700002200999856009001021 2014 eng d00aDecision tree building on multi-core using FastFlow0 aDecision tree building on multicore using FastFlow a800–8200 v263 aThe whole computer hardware industry embraced the multi-core. The extreme optimisation of sequential algorithms is then no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an in-depth study of the parallelisation of an implementation of the C4.5 algorithm for multi-core architectures. We characterise elapsed time lower bounds for the forms of parallelisations adopted and achieve close to optimal performance. Our implementation is based on the FastFlow parallel programming environment, and it requires minimal changes to the original sequential code. Copyright © 2013 John Wiley & Sons, Ltd.1 aAldinucci, Marco1 aRuggieri, Salvatore1 aTorquati, Massimo uhttps://kdd.isti.cnr.it/publications/decision-tree-building-multi-core-using-fastflow00563nas a2200133 4500008004100000245011900041210006900160300001400229490000700243100002100250700001300271700002400284856012100308 2014 eng d00aIntroduction to special issue on computational methods for enforcing privacy and fairness in the knowledge society0 aIntroduction to special issue on computational methods for enfor a109–1110 v221 aMascetti, Sergio1 aRicci, A1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/publications/introduction-special-issue-computational-methods-enforcing-privacy-and-fairness01608nas a2200133 4500008004100000245005800041210005600099300001400155490000700169520116600176100001801342700002401360856009001384 2014 eng d00aA multidisciplinary survey on discrimination analysis0 amultidisciplinary survey on discrimination analysis a582–6380 v293 aThe collection and analysis of observational and experimental data represent the main tools for assessing the presence, the extent, the nature, and the trend of discrimination phenomena. Data analysis techniques have been proposed in the last 50 years in the economic, legal, statistical, and, recently, in the data mining literature. This is not surprising, since discrimination analysis is a multidisciplinary problem, involving sociological causes, legal argumentations, economic models, statistical techniques, and computational issues. The objective of this survey is to provide a guidance and a glue for researchers and anti-discrimination data analysts on concepts, problems, application areas, datasets, methods, and approaches from a multidisciplinary perspective. We organize the approaches according to their method of data collection as observational, quasi-experimental, and experimental studies. A fourth line of recently blooming research on knowledge discovery based methods is also covered. Observational methods are further categorized on the basis of their application context: labor economics, social profiling, consumer markets, and others.1 aRomei, Andrea1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/publications/multidisciplinary-survey-discrimination-analysis01836nas a2200157 4500008004100000245003800041210003500079300001400114490000700128520138300135100002201518700002401540700001701564700002501581856007201606 2014 eng d00aOn quantified linear implications0 aquantified linear implications a301–3250 v713 aA Quantified Linear Implication (QLI) is an inclusion query over two polyhedral sets, with a quantifier string that specifies which variables are existentially quantified and which are universally quantified. Equivalently, it can be viewed as a quantified implication of two systems of linear inequalities. In this paper, we provide a 2-person game semantics for the QLI problem, which allows us to explore the computational complexities of several of its classes. More specifically, we prove that the decision problem for QLIs with an arbitrary number of quantifier alternations is PSPACE-hard. Furthermore, we explore the computational complexities of several classes of 0, 1, and 2-quantifier alternation QLIs. We observed that some classes are decidable in polynomial time, some are NP-complete, some are coNP-hard and some are ΠP2Π2P -hard. We also establish the hardness of QLIs with 2 or more quantifier alternations with respect to the first quantifier in the quantifier string and the number of quantifier alternations. All the proofs that we provide for polynomially solvable problems are constructive, i.e., polynomial-time decision algorithms are devised that utilize well-known procedures. QLIs can be utilized as powerful modelling tools for real-life applications. Such applications include reactive systems, real-time schedulers, and static program analyzers.1 aEirinakis, Pavlos1 aRuggieri, Salvatore1 aSubramani, K1 aWojciechowski, Piotr uhttps://kdd.isti.cnr.it/publications/quantified-linear-implications01114nas a2200121 4500008004100000245006700041210006400108300001300172490000600185520073100191100002400922856004600946 2014 eng d00aUsing t-closeness anonymity to control for non-discrimination.0 aUsing tcloseness anonymity to control for nondiscrimination a99–1290 v73 aWe investigate the relation between t-closeness, a well-known model of data anonymization
against attribute disclosure, and α-protection, a model of the social discrimination hidden in
data. We show that t-closeness implies bdf (t)-protection, for a bound function bdf () depending on
the discrimination measure f() at hand. This allows us to adapt inference control methods, such
as the Mondrian multidimensional generalization technique and the Sabre bucketization and redistribution
framework, to the purpose of non-discrimination data protection. The parallel between
the two analytical models raises intriguing issues on the interplay between data anonymization and
non-discrimination research in data protection.1 aRuggieri, Salvatore uhttp://dl.acm.org/citation.cfm?id=287062300296nas a2200097 4500008004100000245004400041210004300085260000900128100002400137856003700161 2013 eng d00aData Anonymity Meets Non-discrimination0 aData Anonymity Meets Nondiscrimination bIEEE1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/node/63100369nas a2200133 4500008004100000245003600041210003200077260001300109300001300122100002000135700002400155700001900179856003700198 2013 eng d00aThe discovery of discrimination0 adiscovery of discrimination bSpringer a91–1081 aPedreschi, Dino1 aRuggieri, Salvatore1 aTurini, Franco uhttps://kdd.isti.cnr.it/node/63400441nas a2200133 4500008004100000245007600041210006900117300001600186490000700202100001800209700002400227700001900251856003700270 2013 eng d00aDiscrimination discovery in scientific project evaluation: A case study0 aDiscrimination discovery in scientific project evaluation A case a6064–60790 v401 aRomei, Andrea1 aRuggieri, Salvatore1 aTurini, Franco uhttps://kdd.isti.cnr.it/node/63200283nas a2200097 4500008004100000245003400041210003400075260001500109100002400124856003700148 2013 eng d00aLearning from polyhedral sets0 aLearning from polyhedral sets bAAAI Press1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/node/63300344nas a2200109 4500008003900000245003600039210003400075300001200109100001800121700002400139856007100163 2011 d00aWho/Where Are My New Customers?0 aWhoWhere Are My New Customers a307-3171 aRinzivillo, S1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/publications/whowhere-are-my-new-customers00488nas a2200121 4500008003900000245007900039210006900118300001200187100002000199700002400219700001900243856010400262 2009 d00aIntegrating induction and deduction for finding evidence of discrimination0 aIntegrating induction and deduction for finding evidence of disc a157-1661 aPedreschi, Dino1 aRuggieri, Salvatore1 aTurini, Franco uhttps://kdd.isti.cnr.it/content/integrating-induction-and-deduction-finding-evidence-discrimination00468nas a2200121 4500008003900000245006800039210006700107300001200174100002000186700002400206700001900230856009700249 2009 d00aMeasuring Discrimination in Socially-Sensitive Decision Records0 aMeasuring Discrimination in SociallySensitive Decision Records a581-5921 aPedreschi, Dino1 aRuggieri, Salvatore1 aTurini, Franco uhttps://kdd.isti.cnr.it/content/measuring-discrimination-socially-sensitive-decision-records00465nas a2200121 4500008003900000245007000039210006700109300001200176100002000188700001800208700002400226856009300250 2008 d00aA Case Study in Sequential Pattern Mining for IT-Operational Risk0 aCase Study in Sequential Pattern Mining for ITOperational Risk a424-4391 aGrossi, Valerio1 aRomei, Andrea1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/case-study-sequential-pattern-mining-it-operational-risk00378nas a2200121 4500008003900000245003700039210003600076300001200112100002000124700002400144700001900168856006900187 2008 d00aDiscrimination-aware data mining0 aDiscriminationaware data mining a560-5681 aPedreschi, Dino1 aRuggieri, Salvatore1 aTurini, Franco uhttps://kdd.isti.cnr.it/content/discrimination-aware-data-mining00401nas a2200109 4500008003900000245005600039210005400095300001200149100002400161700002400185856008200209 2008 d00aTyping Linear Constraints for Moding CLP() Programs0 aTyping Linear Constraints for Moding CLP Programs a128-1431 aRuggieri, Salvatore1 aMesnard, Frédéric uhttps://kdd.isti.cnr.it/content/typing-linear-constraints-moding-clp-programs00392nas a2200121 4500008004100000245004500041210004500086300001200131490000700143100002000150700002400170856007600194 2004 eng d00aBounded Nondeterminism of Logic Programs0 aBounded Nondeterminism of Logic Programs a313-3430 v421 aPedreschi, Dino1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/bounded-nondeterminism-logic-programs-000440nas a2200121 4500008004100000245005800041210005800099300001200157100002000169700002400189700002100213856008400234 2004 eng d00aCharacterisations of Termination in Logic Programming0 aCharacterisations of Termination in Logic Programming a376-4311 aPedreschi, Dino1 aRuggieri, Salvatore1 aSmaus, Jan-Georg uhttps://kdd.isti.cnr.it/content/characterisations-termination-logic-programming00373nas a2200121 4500008004100000245004200041210003900083300001200122490000700134100002000141700002400161856006600185 2003 eng d00aOn logic programs that always succeed0 alogic programs that always succeed a163-1960 v481 aPedreschi, Dino1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/logic-programs-always-succeed00415nas a2200133 4500008004100000245004200041210004200083300001200125490000600137100002000143700002400163700002100187856007300208 2002 eng d00aClasses of terminating logic programs0 aClasses of terminating logic programs a369-4180 v21 aPedreschi, Dino1 aRuggieri, Salvatore1 aSmaus, Jan-Georg uhttps://kdd.isti.cnr.it/content/classes-terminating-logic-programs-000480nas a2200121 4500008004100000245007100041210006900112300001200181100002200193700002000215700002400235856009900259 2002 eng d00aNegation as Failure through Abduction: Reasoning about Termination0 aNegation as Failure through Abduction Reasoning about Terminatio a240-2721 aMancarella, Paolo1 aPedreschi, Dino1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/negation-failure-through-abduction-reasoning-about-termination00398nas a2200121 4500008004100000245004200041210004200083490001500125100002000140700002400160700002100184856007100205 2001 eng d00aClasses of Terminating Logic Programs0 aClasses of Terminating Logic Programs0 vcs.LO/01061 aPedreschi, Dino1 aRuggieri, Salvatore1 aSmaus, Jan-Georg uhttps://kdd.isti.cnr.it/content/classes-terminating-logic-programs00525nas a2200169 4500008004100000245004400041210004400085300001200129100002200141700002100163700002000184700001800204700001700222700002000239700002400259856007200283 2001 eng d00aData Mining for Intelligent Web Caching0 aData Mining for Intelligent Web Caching a599-6031 aBonchi, Francesco1 aGiannotti, Fosca1 aManco, Giuseppe1 aRenso, Chiara1 aNanni, Mirco1 aPedreschi, Dino1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/data-mining-intelligent-web-caching00527nas a2200169 4500008004100000245004400041210004400085300001200129100002200141700002100163700002000184700001800204700001700222700002000239700002400259856007400283 2001 eng d00aData Mining for Intelligent Web Caching0 aData Mining for Intelligent Web Caching a599-6031 aBonchi, Francesco1 aGiannotti, Fosca1 aManco, Giuseppe1 aRenso, Chiara1 aNanni, Mirco1 aPedreschi, Dino1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/data-mining-intelligent-web-caching-000650nas a2200193 4500008004100000245006800041210006800109300001200177490000700189100002200196700002100218700002000239700002000259700001700279700002000296700001800316700002400334856009800358 2001 eng d00aWeb log data warehousing and mining for intelligent web caching0 aWeb log data warehousing and mining for intelligent web caching a165-1890 v391 aBonchi, Francesco1 aGiannotti, Fosca1 aGozzi, Cristian1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/web-log-data-warehousing-and-mining-intelligent-web-caching-000607nas a2200169 4500008004100000245006800041210006800109100002200177700002100199700002000220700002000240700001700260700002000277700001800297700002400315856009800339 2001 eng d00aWeb Log Data Warehousing and Mining for Intelligent Web Caching0 aWeb Log Data Warehousing and Mining for Intelligent Web Caching1 aBonchi, Francesco1 aGiannotti, Fosca1 aGozzi, Cristian1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/web-log-data-warehousing-and-mining-intelligent-web-caching-100648nas a2200193 4500008004100000245006800041210006800109300001200177490000700189100002200196700002100218700002000239700002000259700001700279700002000296700001800316700002400334856009600358 2001 eng d00aWeb log data warehousing and mining for intelligent web caching0 aWeb log data warehousing and mining for intelligent web caching a165-1890 v391 aBonchi, Francesco1 aGiannotti, Fosca1 aGozzi, Cristian1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/web-log-data-warehousing-and-mining-intelligent-web-caching00371nas a2200109 4500008004100000245004500041210004500086300001200131100002000143700002400163856007400187 1999 eng d00aBounded Nondeterminism of Logic Programs0 aBounded Nondeterminism of Logic Programs a350-3641 aPedreschi, Dino1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/bounded-nondeterminism-logic-programs00340nas a2200109 4500008004100000245003900041210003600080490000700116100002000123700002400143856006300167 1999 eng d00aOn Logic Programs That Do Not Fail0 aLogic Programs That Do Not Fail0 v301 aPedreschi, Dino1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/logic-programs-do-not-fail00360nas a2200121 4500008004100000245003500041210003500076300001200111490000700123100002000130700002400150856006400174 1999 eng d00aVerification of Logic Programs0 aVerification of Logic Programs a125-1760 v391 aPedreschi, Dino1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/verification-logic-programs00358nas a2200097 4500008004100000245005100041210004900092100001800141700002400159856007700183 1998 eng d00aA Mediator Approach for Representing Knowledge0 aMediator Approach for Representing Knowledge1 aRenso, Chiara1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/mediator-approach-representing-knowledge00407nas a2200121 4500008004100000245005100041210005100092300001200143490000700155100002000162700002400182856007900206 1998 eng d00aWeakest Preconditions for Pure Prolog Programs0 aWeakest Preconditions for Pure Prolog Programs a145-1500 v671 aPedreschi, Dino1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/weakest-preconditions-pure-prolog-programs00367nas a2200121 4500008004100000245003800041210003700079300001200116490000600128100002000134700002400154856006700178 1997 eng d00aVerification of Meta-Interpreters0 aVerification of MetaInterpreters a267-3030 v71 aPedreschi, Dino1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/verification-meta-interpreters00450nas a2200109 4500008004100000245007600041210006900117300001200186100002000198700002400218856009800242 1995 eng d00aA Case Study in Logic Program Verification: the Vanilla Metainterpreter0 aCase Study in Logic Program Verification the Vanilla Metainterpr a643-6541 aPedreschi, Dino1 aRuggieri, Salvatore uhttps://kdd.isti.cnr.it/content/case-study-logic-program-verification-vanilla-metainterpreter