02707nas a2200145   4500008004100000245005000041210004700091260006400138520221100202100002102413700002202434700002202456700002002478856006302498       2018                        eng d00aSoBigData: Social Mining & Big Data Ecosystem0 aSoBigData Social Mining Big Data Ecosystem  bInternational World Wide Web Conferences Steering Committee3 aOne of the most pressing and fascinating challenges scientists face today, is understanding the complexity of our globally interconnected society. The big data arising from the digital breadcrumbs of human activities has the potential of providing a powerful social microscope, which can help us understand many complex and hidden socio-economic phenomena. Such challenge requires high-level analytics, modeling and reasoning across all the social dimensions above. There is a need to harness these opportunities for scientific advancement and for the social good, compared to the currently prevalent exploitation of big data for commercial purposes or, worse, social control and surveillance. The main obstacle to this accomplishment, besides the scarcity of data scientists, is the lack of a large-scale open ecosystem where big data and social mining research can be carried out. The SoBigData Research Infrastructure (RI) provides an integrated ecosystem for ethic-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life as recorded by "big data". The research community uses the SoBigData facilities as a "secure digital wind-tunnel" for large-scale social data analysis and simulation experiments. SoBigData promotes repeatable and open science and supports data science research projects by providing: i) an ever-growing, distributed data ecosystem for procurement, access and curation and management of big social data, to underpin social data mining research within an ethic-sensitive context; ii) an ever-growing, distributed platform of interoperable, social data mining methods and associated skills: tools, methodologies and services for mining, analysing, and visualising complex and massive datasets, harnessing the techno-legal barriers to the ethically safe deployment of big data for social mining; iii) an ecosystem where protection of personal information and the respect for fundamental human rights can coexist with a safe use of the same information for scientific purposes of broad and central societal interest. SoBigData has a dedicated ethical and legal board, which is implementing a legal and ethical framework.1 aGiannotti, Fosca1 aTrasarti, Roberto1 aBontcheva, Kalina1 aGrossi, Valerio  uhttp://www.sobigdata.eu/sites/default/files/www%202018.pdf01602nas a2200157   4500008004100000245007000041210006800111490000700179520110500186100002201291700001801313700002101331700002001352700002201372856005001394       2017                        eng d00aHyWare: a HYbrid Workflow lAnguage for Research E-infrastructures0 aHyWare a HYbrid Workflow lAnguage for Research Einfrastructures0 v233 aResearch e-infrastructures are "systems of systems", patchworks of tools, services and data sources, evolving over time to address the needs of the scientific process. Accordingly, in such environments, researchers implement their scientific processes by means of workflows made of a variety of actions, including for example usage of web services, download and execution of shared software libraries or tools, or local and manual manipulation of data. Although scientists may benefit from sharing their scientific process, the heterogeneity underpinning e-infrastructures hinders their ability to represent, share and eventually reproduce such workflows. This work presents HyWare, a language for representing scientific process in highly-heterogeneous e-infrastructures in terms of so-called hybrid workflows. HyWare lays in between "business process modeling languages", which offer a formal and high-level description of a reasoning, protocol, or procedure, and "workflow execution languages", which enable the fully automated execution of a sequence of computational steps via dedicated engines.1 aCandela, Leonardo1 aManghi, Paolo1 aGiannotti, Fosca1 aGrossi, Valerio1 aTrasarti, Roberto  uhttp://dx.doi.org/10.1045/january2017-candela01187nas a2200145   4500008004100000245004700041210004700088300001400135490000700149520075000156100002000906700001800926700001900944856007800963       2017                        eng d00aSurvey on using constraints in data mining0 aSurvey on using constraints in data mining  a424–4640 v313 aThis 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.1 aGrossi, Valerio1 aRomei, Andrea1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/survey-using-constraints-data-mining00937nas a2200145   4500008004100000245004500041210004400086260003800130300001200168520047400180100002000654700002000674700001900694856007800713       2016                        eng d00aData Mining and Constraints: An Overview0 aData Mining and Constraints An Overview  bSpringer International Publishing  a25–483 aThis 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.1 aGrossi, Valerio1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/data-mining-and-constraints-overview01899nas a2200169   4500008004100000245007000041210006900111300001700180490000600197520138900203100001701592700002201609700001901631700002001650700002001670856003901690       2016                        eng d00aDriving Profiles Computation and Monitoring for Car Insurance CRM0 aDriving Profiles Computation and Monitoring for Car Insurance CR  a14:1–14:260 v83 aCustomer segmentation is one of the most traditional and valued tasks in customer relationship management (CRM). In this article, we explore the problem in the context of the car insurance industry, where the mobility behavior of customers plays a key role: Different mobility needs, driving habits, and skills imply also different requirements (level of coverage provided by the insurance) and risks (of accidents). In the present work, we describe a methodology to extract several indicators describing the driving profile of customers, and we provide a clustering-oriented instantiation of the segmentation problem based on such indicators. Then, we consider the availability of a continuous flow of fresh mobility data sent by the circulating vehicles, aiming at keeping our segments constantly up to date. We tackle a major scalability issue that emerges in this context when the number of customers is large-namely, the communication bottleneck-by proposing and implementing a sophisticated distributed monitoring solution that reduces communications between vehicles and company servers to the essential. We validate the framework on a large database of real mobility data coming from GPS devices on private cars. Finally, we analyze the privacy risks that the proposed approach might involve for the users, providing and evaluating a countermeasure based on data perturbation.1 aNanni, Mirco1 aTrasarti, Roberto1 aMonreale, Anna1 aGrossi, Valerio1 aPedreschi, Dino  uhttp://doi.acm.org/10.1145/291214801341nas a2200169   4500008004100000245006100041210006000102260003800162300001400200520081200214100002001026700001501046700001901061700001701080700002301097856005101120       2016                        eng d00aPartition-Based Clustering Using Constraint Optimization0 aPartitionBased Clustering Using Constraint Optimization  bSpringer International Publishing  a282–2993 aPartition-based clustering is the task of partitioning a dataset in a number of groups of examples, such that examples in each group are similar to each other. Many criteria for what constitutes a good clustering have been identified in the literature; furthermore, the use of additional constraints to find more useful clusterings has been proposed. In this chapter, it will be shown that most of these clustering tasks can be formalized using optimization criteria and constraints. We demonstrate how a range of clustering tasks can be modelled in generic constraint programming languages with these constraints and optimization criteria. Using the constraint-based modeling approach we also relate the DBSCAN method for density-based clustering to the label propagation technique for community discovery.1 aGrossi, Valerio1 aGuns, Tias1 aMonreale, Anna1 aNanni, Mirco1 aNijssen, Siegfried  uhttp://dx.doi.org/10.1007/978-3-319-50137-6_1101521nas a2200157   4500008004100000245005700041210005700098260003100155520103200186100002001218700001901238700001701257700002001274700001901294856005001313       2015                        eng d00aClustering Formulation Using Constraint Optimization0 aClustering Formulation Using Constraint Optimization  bSpringer Berlin Heidelberg3 aThe 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.1 aGrossi, Valerio1 aMonreale, Anna1 aNanni, Mirco1 aPedreschi, Dino1 aTurini, Franco  uhttp://dx.doi.org/10.1007/978-3-662-49224-6_900465nas 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-risk00627nas a2200145   4500008003900000020002200039245008800061210006900149100001900218700002100237700002200258700002000280700001800300856016300318       2008                          d  a978-953-7619-16-900aDiscovering Strategic Behaviour in Multi- Agent Scenarios by Ontology-Driven Mining0 aDiscovering Strategic Behaviour in Multi Agent Scenarios by Onto1 aBacciu, Davide1 aBellandi, Andrea1 aFurletti, Barbara1 aGrossi, Valerio1 aRomei, Andrea  uhttp://www.intechopen.com/books/advances_in_robotics_automation_and_control/discovering_strategic_behaviors_in_multi-agent_scenarios_by_ontology-driven_mining00467nas a2200133   4500008003900000245005200039210005200091260002400143100002200167700002100189700002000210700001800230856008500248       2008                          d00aOntological Support for Association Rule Mining0 aOntological Support for Association Rule Mining  aInnsbruck, Austria 1 aFurletti, Barbara1 aBellandi, Andrea1 aGrossi, Valerio1 aRomei, Andrea  uhttps://kdd.isti.cnr.it/publications/ontological-support-association-rule-mining00446nas a2200133   4500008003900000245006200039210006000101260002800161100002200189700002100211700002000232700001800252856004200270       2007                          d00aOntology-Driven Association Rule Extraction: A Case Study0 aOntologyDriven Association Rule Extraction A Case Study  aRoskilde, Denmarkc20071 aFurletti, Barbara1 aBellandi, Andrea1 aGrossi, Valerio1 aRomei, Andrea  uhttp://ceur-ws.org/Vol-298/paper1.pdf00577nas a2200145   4500008003900000020002200039245008000061210006900141260000900210100002200219700002100241700001800262700002000280856013100300       2007                          d  a978-972-8924-44-700aPUSHING CONSTRAINTS IN ASSOCIATION RULE MINING: AN ONTOLOGY-BASED APPROACH 0 aPUSHING CONSTRAINTS IN ASSOCIATION RULE MINING AN ONTOLOGYBASED   c20071 aFurletti, Barbara1 aBellandi, Andrea1 aRomei, Andrea1 aGrossi, Valerio  uhttp://www.iadisportal.org/digital-library/mdownload/pushing-constraints-in-association-rule-mining-an-ontology-based-approach