01507nas a2200121 4500008004100000245007100041210006900112300001200181520108400193100002101277700001801298856006901316 2017 eng d00aQuantifying the relation between performance and success in soccer0 aQuantifying the relation between performance and success in socc a17500143 aThe availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this study, we analyze more than 6000 games and 10 million events in six European leagues and investigate this relation in soccer competitions. We discover that a team’s position in a competition’s final ranking is significantly related to its typical performance, as described by a set of technical features extracted from the soccer data. Moreover, we find that, while victory and defeats can be explained by the team’s performance during a game, it is difficult to detect draws by using a machine learning approach. We then simulate the outcomes of an entire season of each league only relying on technical data and exploiting a machine learning model trained on data from past seasons. The simulation produces a team ranking which is similar to the actual ranking, suggesting that a complex systems’ view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.1 aPappalardo, Luca1 aCintia, Paolo uhttp://www.worldscientific.com/doi/abs/10.1142/S021952591750014X01921nas a2200169 4500008004100000245003800041210003800079260002400117520140400141100001901545700001901564700002101583700002001604700002101624700002501645856008101670 2015 eng d00aQuantification in Social Networks0 aQuantification in Social Networks aParis, FrancebIEEE3 aIn many real-world applications there is a need to monitor the distribution of a population across different classes, and to track changes in this distribution over time. As an example, an important task is to monitor the percentage of unemployed adults in a given region. When the membership of an individual in a class cannot be established deterministically, a typical solution is the classification task. However, in the above applications the final goal is not determining which class the individuals belong to, but estimating the prevalence of each class in the unlabeled data. This task is called quantification. Most of the work in the literature addressed the quantification problem considering data presented in conventional attribute format. Since the ever-growing availability of web and social media we have a flourish of network data representing a new important source of information and by using quantification network techniques we could quantify collective behavior, i.e., the number of users that are involved in certain type of activities, preferences, or behaviors. In this paper we exploit the homophily effect observed in many social networks in order to construct a quantifier for networked data. Our experiments show the effectiveness of the proposed approaches and the comparison with the existing state-of-the-art quantification methods shows that they are more accurate. 1 aMilli, Letizia1 aMonreale, Anna1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca1 aSebastiani, Fabrizio uhttp://www.giuliorossetti.net/about/wp-content/uploads/2015/12/main_DSAA.pdf01836nas 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-implications02131nas a2200169 4500008003900000245002500039210002500064300001400089520168900103100001901792700001901811700002101830700002101851700002001872700002501892856004401917 2013 d00aQuantification Trees0 aQuantification Trees a528–5363 aIn many applications there is a need to monitor how a population is distributed across different classes, and to track the changes in this distribution that derive from varying circumstances, an example such application is monitoring the percentage (or "prevalence") of unemployed people in a given region, or in a given age range, or at different time periods. When the membership of an individual in a class cannot be established deterministically, this monitoring activity requires classification. However, in the above applications the final goal is not determining which class each individual belongs to, but simply estimating the prevalence of each class in the unlabeled data. This task is called quantification. In a supervised learning framework we may estimate the distribution across the classes in a test set from a training set of labeled individuals. However, this may be sub optimal, since the distribution in the test set may be substantially different from that in the training set (a phenomenon called distribution drift). So far, quantification has mostly been addressed by learning a classifier optimized for individual classification and later adjusting the distribution it computes to compensate for its tendency to either under-or over-estimate the prevalence of the class. In this paper we propose instead to use a type of decision trees (quantification trees) optimized not for individual classification, but directly for quantification. Our experiments show that quantification trees are more accurate than existing state-of-the-art quantification methods, while retaining at the same time the simplicity and understandability of the decision tree framework.1 aMilli, Letizia1 aMonreale, Anna1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino1 aSebastiani, Fabrizio uhttp://dx.doi.org/10.1109/ICDM.2013.12200478nas a2200157 4500008003900000245004600039210004400085300001000129490000600139100002200145700002100167700001700188700002000205700001800225856007700243 2011 d00aA Query Language for Mobility Data Mining0 aQuery Language for Mobility Data Mining a24-450 v71 aTrasarti, Roberto1 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara uhttps://kdd.isti.cnr.it/publications/query-language-mobility-data-mining00497nas a2200109 4500008003900000245010500039210006900144300001200213100001700225700002200242856012300264 2010 d00aQuerying and mining trajectories with gaps: a multi-path reconstruction approach (Extended Abstract)0 aQuerying and mining trajectories with gaps a multipath reconstru a126-1331 aNanni, Mirco1 aTrasarti, Roberto uhttps://kdd.isti.cnr.it/publications/querying-and-mining-trajectories-gaps-multi-path-reconstruction-approach-extended00536nas a2200157 4500008003900000245005800039210005800097300001200155100002000167700002100187700002100208700001900229700002600248700001800274856008600292 2008 d00aQuerying and Reasoning for Spatiotemporal Data Mining0 aQuerying and Reasoning for Spatiotemporal Data Mining a335-3741 aManco, Giuseppe1 aBaglioni, Miriam1 aGiannotti, Fosca1 aKuijpers, Bart1 aRaffaetà, Alessandra1 aRenso, Chiara uhttps://kdd.isti.cnr.it/content/querying-and-reasoning-spatiotemporal-data-mining00595nas a2200157 4500008004100000245005900041210005800100260006700158100002000225700002100245700002100266700001900287700002600306700001800332856008700350 2008 eng d00aQuerying and Reasoning for Spatio-Temporal Data Mining0 aQuerying and Reasoning for SpatioTemporal Data Mining aMobility, Privacy, and Geographyba Knowledge Discovery vision1 aManco, Giuseppe1 aBaglioni, Miriam1 aGiannotti, Fosca1 aKuijpers, Bart1 aRaffaetà, Alessandra1 aRenso, Chiara uhttps://kdd.isti.cnr.it/content/querying-and-reasoning-spatio-temporal-data-mining00434nas a2200121 4500008004100000245005700041210005700098300001000155100002600165700001800191700001900209856008400228 2003 eng d00aQualitative Spatial Reasoning in a Logical Framework0 aQualitative Spatial Reasoning in a Logical Framework a78-901 aRaffaetà, Alessandra1 aRenso, Chiara1 aTurini, Franco uhttps://kdd.isti.cnr.it/content/qualitative-spatial-reasoning-logical-framework00432nas a2200121 4500008004100000245005600041210005500097300001200152100002600164700001800190700001900208856008300227 2002 eng d00aQualitative Reasoning in a Spatio-Temporal Language0 aQualitative Reasoning in a SpatioTemporal Language a105-1181 aRaffaetà, Alessandra1 aRenso, Chiara1 aTurini, Franco uhttps://kdd.isti.cnr.it/content/qualitative-reasoning-spatio-temporal-language00449nas a2200109 4500008004100000245007300041210006900114300001200183100002100195700002000216856010300236 1999 eng d00aQuerying inductive Databases via Logic-Based user-defined aggregates0 aQuerying inductive Databases via LogicBased userdefined aggregat a605-6201 aGiannotti, Fosca1 aManco, Giuseppe uhttps://kdd.isti.cnr.it/content/querying-inductive-databases-logic-based-user-defined-aggregates-000447nas a2200109 4500008004100000245007300041210006900114300001200183100002100195700002000216856010100236 1999 eng d00aQuerying Inductive Databases via Logic-Based User-Defined Aggregates0 aQuerying Inductive Databases via LogicBased UserDefined Aggregat a125-1351 aGiannotti, Fosca1 aManco, Giuseppe uhttps://kdd.isti.cnr.it/content/querying-inductive-databases-logic-based-user-defined-aggregates00502nas a2200133 4500008004100000245007000041210006900111300001200180100002100192700002000213700001700233700002000250856009800270 1998 eng d00aQuery Answering in Nondeterministic, Nonmonotonic Logic Databases0 aQuery Answering in Nondeterministic Nonmonotonic Logic Databases a175-1871 aGiannotti, Fosca1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino uhttps://kdd.isti.cnr.it/content/query-answering-nondeterministic-nonmonotonic-logic-databases