00937nas 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/10.1145/291214800627nas 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_mining