@conference {1493, title = {Connected Vehicle Simulation Framework for Parking Occupancy Prediction (Demo Paper)}, booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems}, year = {2022}, publisher = {Association for Computing Machinery}, organization = {Association for Computing Machinery}, address = {New York, NY, USA}, abstract = {This paper demonstrates a simulation framework that collects data about connected vehicles{\textquoteright} locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications.}, isbn = {9781450395298}, doi = {10.1145/3557915.3560995}, url = {https://doi.org/10.1145/3557915.3560995}, author = {Resce, Pierpaolo and Vorwerk, Lukas and Han, Zhiwei and Cornacchia, Giuliano and Alamdari, Omid Isfahani and Mirco Nanni and Luca Pappalardo and Weimer, Daniel and Liu, Yuanting} } @article {1484, title = {Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities}, journal = {arXiv preprint arXiv:2110.15269}, year = {2021}, author = {Joseph, Simmi Marina and Salvatore Citraro and Morini, Virginia and Giulio Rossetti and Stella, Massimo} } @article {1429, title = {Conformity: a Path-Aware Homophily measure for Node-Attributed Networks}, journal = {IEEE Intelligent SystemsIEEE Intelligent Systems}, year = {2021}, month = {2021}, pages = {1 - 1}, abstract = {Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Differently from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.}, isbn = {1941-1294}, doi = {https://doi.org/10.1109/MIS.2021.3051291}, url = {https://ieeexplore.ieee.org/document/9321348}, author = {Giulio Rossetti and Salvatore Citraro and Letizia Milli} } @conference {1371, title = {Capturing Political Polarization of Reddit Submissions in the Trump Era}, booktitle = {SEBD}, year = {2020}, author = {Giulio Rossetti and Morini, Virginia and Pollacci, Laura} } @article {1423, title = {Causal inference for social discrimination reasoning}, volume = {54}, year = {2020}, month = {2020/04/01}, pages = {425 - 437}, abstract = {The discovery of discriminatory bias in human or automated decision making is a task of increasing importance and difficulty, exacerbated by the pervasive use of machine learning and data mining. Currently, discrimination discovery largely relies upon correlation analysis of decisions records, disregarding the impact of confounding biases. We present a method for causal discrimination discovery based on propensity score analysis, a statistical tool for filtering out the effect of confounding variables. We introduce causal measures of discrimination which quantify the effect of group membership on the decisions, and highlight causal discrimination/favoritism patterns by learning regression trees over the novel measures. We validate our approach on two real world datasets. Our proposed framework for causal discrimination has the potential to enhance the transparency of machine learning with tools for detecting discriminatory bias both in the training data and in the learning algorithms.}, isbn = {1573-7675}, doi = {https://doi.org/10.1007/s10844-019-00580-x}, url = {https://link.springer.com/article/10.1007/s10844-019-00580-x}, author = {Qureshi, Bilal and Kamiran, Faisal and Karim, Asim and Salvatore Ruggieri and Dino Pedreschi} } @article {1364, title = {Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks}, journal = {arXiv preprint arXiv:2012.05195}, year = {2020}, author = {Giulio Rossetti and Salvatore Citraro and Letizia Milli} } @article {1296, title = {Causal inference for social discrimination reasoning}, journal = {Journal of Intelligent Information Systems}, year = {2019}, pages = {1{\textendash}13}, abstract = {The discovery of discriminatory bias in human or automated decision making is a task of increasing importance and difficulty, exacerbated by the pervasive use of machine learning and data mining. Currently, discrimination discovery largely relies upon correlation analysis of decisions records, disregarding the impact of confounding biases. We present a method for causal discrimination discovery based on propensity score analysis, a statistical tool for filtering out the effect of confounding variables. We introduce causal measures of discrimination which quantify the effect of group membership on the decisions, and highlight causal discrimination/favoritism patterns by learning regression trees over the novel measures. We validate our approach on two real world datasets. Our proposed framework for causal discrimination has the potential to enhance the transparency of machine learning with tools for detecting discriminatory bias both in the training data and in the learning algorithms.}, doi = {10.1007/s10844-019-00580-x}, url = {https://link.springer.com/article/10.1007/s10844-019-00580-x}, author = {Qureshi, Bilal and Kamiran, Faisal and Karim, Asim and Salvatore Ruggieri and Dino Pedreschi} } @article {1407, title = {CDLIB: a python library to extract, compare and evaluate communities from complex networks}, journal = {Applied Network Science}, volume = {4}, year = {2019}, month = {2019/07/29}, pages = {52}, abstract = {Community Discovery is among the most studied problems in complex network analysis. During the last decade, many algorithms have been proposed to address such task; however, only a few of them have been integrated into a common framework, making it hard to use and compare different solutions. To support developers, researchers and practitioners, in this paper we introduce a python library - namely CDlib - designed to serve this need. The aim of CDlib is to allow easy and standardized access to a wide variety of network clustering algorithms, to evaluate and compare the results they provide, and to visualize them. It notably provides the largest available collection of community detection implementations, with a total of 39 algorithms.}, isbn = {2364-8228}, doi = {https://doi.org/10.1007/s41109-019-0165-9}, url = {https://link.springer.com/article/10.1007/s41109-019-0165-9}, author = {Giulio Rossetti and Letizia Milli and Cazabet, R{\'e}my} } @inbook {1293, title = {Challenges in community discovery on temporal networks}, booktitle = {Temporal Network Theory}, year = {2019}, pages = {181{\textendash}197}, publisher = {Springer}, organization = {Springer}, abstract = {Community discovery is one of the most studied problems in network science. In recent years, many works have focused on discovering communities in temporal networks, thus identifying dynamic communities. Interestingly, dynamic communities are not mere sequences of static ones; new challenges arise from their dynamic nature. Despite the large number of algorithms introduced in the literature, some of these challenges have been overlooked or little studied until recently. In this chapter, we will discuss some of these challenges and recent propositions to tackle them. We will, among other topics, discuss of community events in gradually evolving networks, on the notion of identity through change and the ship of Theseus paradox, on dynamic communities in different types of networks including link streams, on the smoothness of dynamic communities, and on the different types of complexity of algorithms for their discovery. We will also list available tools and libraries adapted to work with this problem.}, doi = {10.1007/978-3-030-23495-9_10}, url = {https://link.springer.com/chapter/10.1007/978-3-030-23495-9_10}, author = {Cazabet, R{\'e}my and Giulio Rossetti} } @conference {1372, title = {Community-Aware Content Diffusion: Embeddednes and Permeability}, booktitle = {International Conference on Complex Networks and Their Applications}, year = {2019}, publisher = {Springer}, organization = {Springer}, author = {Letizia Milli and Giulio Rossetti} } @conference {1379, title = {A complex network approach to semantic spaces: How meaning organizes itself}, booktitle = {SEBD}, year = {2019}, author = {Salvatore Citraro and Giulio Rossetti} } @article {999, title = {Community Discovery in Dynamic Networks: a Survey}, journal = {Journal ACM Computing Surveys}, volume = {51}, number = {2}, year = {2018}, abstract = {Networks built to model real world phenomena are characeterised by some properties that have attracted the attention of the scientific community: (i) they are organised according to community structure and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and challenging problem started capturing researcher interest recently: the identification of evolving communities. To model the evolution of a system, dynamic networks can be used: nodes and edges are mutable and their presence, or absence, deeply impacts the community structure that composes them. The aim of this survey is to present the distinctive features and challenges of dynamic community discovery, and propose a classification of published approaches. As a "user manual", this work organizes state of art methodologies into a taxonomy, based on their rationale, and their specific instanciation. Given a desired definition of network dynamics, community characteristics and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers to choose in which direction should future research be oriented. }, doi = {10.1145/3172867}, url = {https://dl.acm.org/citation.cfm?id=3172867}, author = {Giulio Rossetti and Cazabet, R{\'e}my} } @conference {953, title = {Clustering Individual Transactional Data for Masses of Users}, booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, year = {2017}, publisher = {ACM}, organization = {ACM}, abstract = {Mining a large number of datasets recording human activities for making sense of individual data is the key enabler of a new wave of personalized knowledge-based services. In this paper we focus on the problem of clustering individual transactional data for a large mass of users. Transactional data is a very pervasive kind of information that is collected by several services, often involving huge pools of users. We propose txmeans, a parameter-free clustering algorithm able to efficiently partitioning transactional data in a completely automatic way. Txmeans is designed for the case where clustering must be applied on a massive number of different datasets, for instance when a large set of users need to be analyzed individually and each of them has generated a long history of transactions. A deep experimentation on both real and synthetic datasets shows the practical effectiveness of txmeans for the mass clustering of different personal datasets, and suggests that txmeans outperforms existing methods in terms of quality and efficiency. Finally, we present a personal cart assistant application based on txmeans}, doi = {10.1145/3097983.3098034}, author = {Riccardo Guidotti and Anna Monreale and Mirco Nanni and Fosca Giannotti and Dino Pedreschi} } @article {972, title = {Causal Discrimination Discovery Through Propensity Score Analysis}, journal = {arXiv preprint arXiv:1608.03735}, year = {2016}, abstract = {Social discrimination is considered illegal and unethical in the modern world. Such discrimination is often implicit in observed decisions{\textquoteright} 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.}, url = {https://arxiv.org/abs/1608.03735}, author = {Qureshi, Bilal and Kamiran, Faisal and Karim, Asim and Salvatore Ruggieri} } @conference {968, title = {Classification Rule Mining Supported by Ontology for Discrimination Discovery}, booktitle = {Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on}, year = {2016}, publisher = {IEEE}, organization = {IEEE}, abstract = {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.}, doi = {10.1109/ICDMW.2016.0128}, author = {Luong, Binh Thanh and Salvatore Ruggieri and Franco Turini} } @conference {756, title = {City users{\textquoteright} classification with mobile phone data}, booktitle = {IEEE Big Data}, year = {2015}, month = {11/2015}, address = {Santa Clara (CA) - USA}, abstract = {Nowadays mobile phone data are an actual proxy for studying the users{\textquoteright} social life and urban dynamics. In this paper we present the Sociometer, and analytical framework aimed at classifying mobile phone users into behavioral categories by means of their call habits. The analytical process starts from spatio-temporal profiles, learns the different behaviors, and returns annotated profiles. After the description of the methodology and its evaluation, we present an application of the Sociometer for studying city users of one small and one big city, evaluating the impact of big events in these cities.}, author = {Lorenzo Gabrielli and Barbara Furletti and Roberto Trasarti and Fosca Giannotti and Dino Pedreschi} } @conference {878, title = {Clustering Formulation Using Constraint Optimization}, booktitle = {Software Engineering and Formal Methods - {SEFM} 2015 Collocated Workshops: ATSE, HOFM, MoKMaSD, and VERY*SCART, York, UK, September 7-8, 2015, Revised Selected Papers}, year = {2015}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, abstract = {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.}, doi = {10.1007/978-3-662-49224-6_9}, url = {http://dx.doi.org/10.1007/978-3-662-49224-6_9}, author = {Valerio Grossi and Anna Monreale and Mirco Nanni and Dino Pedreschi and Franco Turini} } @conference {774, title = {ComeWithMe: An Activity-Oriented Carpooling Approach}, booktitle = {2015 {IEEE} 18th International Conference on Intelligent Transportation Systems}, year = {2015}, month = {09/2015}, publisher = {Institute of Electrical {\&} Electronics Engineers ({IEEE})}, organization = {Institute of Electrical {\&} Electronics Engineers ({IEEE})}, abstract = {The interest in carpooling is increasing due to the need to reduce traffic and noise pollution. Most of the available approaches and systems are route oriented, where driver and passengers are matched when the destination location is the same. ComeWithMe offers a new perspective: the destination is the intended activity instead of a location. This novel matching method is aimed to boost the possibilities of rides if passenger reaches a different location maintaining the activity. We conducted experiments using a real data set of trajectories and our results showed that the proposed matching algorithm improved the traditional carpooling approach in more than 80\%.}, doi = {10.1109/itsc.2015.414}, url = {http://dx.doi.org/10.1109/itsc.2015.414}, author = {Vinicius Monteiro de Lira and Val{\'e}ria Ces{\'a}rio Times and Chiara Renso and S Rinzivillo} } @conference {819, title = {Community-centric analysis of user engagement in Skype social network}, booktitle = {International conference on Advances in Social Network Analysis and Mining}, year = {2015}, publisher = {IEEE}, organization = {IEEE}, address = {Paris, France}, isbn = {978-1-4503-3854-7}, doi = {10.1145/2808797.2809384}, url = {http://dl.acm.org/citation.cfm?doid=2808797.2809384}, author = {Giulio Rossetti and Luca Pappalardo and Riivo Kikas and Dino Pedreschi and Fosca Giannotti and Marlon Dumas} } @conference {637, title = {CF-inspired Privacy-Preserving Prediction of Next Location in the Cloud}, booktitle = {Cloud Computing Technology and Science (CloudCom), 2014 IEEE 6th International Conference on}, year = {2014}, publisher = {IEEE}, organization = {IEEE}, abstract = {Mobility data gathered from location sensors such as Global Positioning System (GPS) enabled phones and vehicles is valuable for spatio-temporal data mining for various location-based services (LBS). Such data is often considered sensitive and there exist many a mechanism for privacy preserving analyses of the data. Through various anonymisation mechanisms, it can be ensured with a high probability that a particular individual cannot be identified when mobility data is outsourced to third parties for analysis. However, challenges remain with the privacy of the queries on outsourced analysis results, especially when the queries are sent directly to third parties by end-users. Drawing inspiration from our earlier work in privacy preserving collaborative filtering (CF) and next location prediction, in this exploratory work, we propose a novel representation of trajectory data in the CF domain and experiment with a privacy preserving Slope One CF predictor. We present evaluations for the accuracy and the computational performance of our proposal using anonymised data gathered from real traffic data in the Italian cities of Pisa and Milan. One use-case is a third-party location-prediction-as-a-service deployed on a public cloud, which can respond to privacy-preserving queries while enabling data owners to build a rich predictor on the cloud. }, doi = {10.1109/CloudCom.2014.114}, url = {http://dx.doi.org/10.1109/CloudCom.2014.114}, author = {Anirban Basu and Juan Camilo Corena and Anna Monreale and Dino Pedreschi and Fosca Giannotti and Shinsaku Kiyomoto and Vaidya, Jaideep and Yutaka Miyake} } @proceedings {793, title = {The CoLing Lab system for Sentiment Polarity Classification of tweets}, volume = {II}, year = {2014}, author = {Lucia Passaro and Lebani, Gianluca E and Pollacci, Laura and Chersoni, Emmanuele and Lenci, Alessandro} } @article {988, title = {On the complexity of quantified linear systems}, journal = {Theoretical Computer Science}, volume = {518}, year = {2014}, pages = {128{\textendash}134}, abstract = {In 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.}, doi = {10.1016/j.tcs.2013.08.001}, author = {Salvatore Ruggieri and Eirinakis, Pavlos and Subramani, K and Wojciechowski, Piotr} } @article {810, title = {Cohesion, consensus and extreme information in opinion dynamics}, journal = {Advances in Complex Systems}, volume = {16}, number = {06}, year = {2013}, pages = {1350035}, doi = {10.1142/S0219525913500355}, url = {http://www.worldscientific.com/doi/abs/10.1142/S0219525913500355}, author = {Alina Sirbu and Vittorio Loreto and Vito D P Servedio and Francesca Tria} } @conference {731, title = {Comparing General Mobility and Mobility by Car}, booktitle = {Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI CBIC), 2013 BRICS Congress on}, year = {2013}, month = {Sept}, doi = {10.1109/BRICS-CCI-CBIC.2013.116}, author = {Luca Pappalardo and Filippo Simini and S Rinzivillo and Dino Pedreschi and Fosca Giannotti} } @article {531, title = {CONSTAnT - A Conceptual Data Model for Semantic Trajectories of Moving Objects }, journal = {Transaction in GIS}, year = {2013}, author = {Vania Bogorny and Chiara Renso and Artur Ribeiro de Aquino and Fernando de Lucca Siqueira and Luis Otavio Alvares} } @conference {570, title = {Classifying Trust/Distrust Relationships in Online Social Networks}, booktitle = {2012 International Conference on Privacy, Security, Risk and Trust, {PASSAT} 2012, and 2012 International Confernece on Social Computing, SocialCom 2012, Amsterdam, Netherlands, September 3-5, 2012}, year = {2012}, pages = {552{\textendash}557}, abstract = {Online social networks are increasingly being used as places where communities gather to exchange information, form opinions, collaborate in response to events. An aspect of this information exchange is how to determine if a source of social information can be trusted or not. Data mining literature addresses this problem. However, if usually employs social balance theories, by looking at small structures in complex networks known as triangles. This has proven effective in some cases, but it under performs in the lack of context information about the relation and in more complex interactive structures. In this paper we address the problem of creating a framework for the trust inference, able to infer the trust/distrust relationships in those relational environments that cannot be described by using the classical social balance theory. We do so by decomposing a trust network in its ego network components and mining on this ego network set the trust relationships, extending a well known graph mining algorithm. We test our framework on three public datasets describing trust relationships in the real world (from the social media Epinions, Slash dot and Wikipedia) and confronting our results with the trust inference state of the art, showing better performances where the social balance theory fails.}, doi = {10.1109/SocialCom-PASSAT.2012.115}, url = {http://dx.doi.org/10.1109/SocialCom-PASSAT.2012.115}, author = {Giacomo Bachi and Michele Coscia and Anna Monreale and Fosca Giannotti} } @proceedings {545, title = {ComeTogether: Discovering Communities of Places in Mobility Data}, year = {2012}, month = {2012}, pages = { 268-273}, author = {Igo Brilhante and Michele Berlingerio and Roberto Trasarti and Chiara Renso and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Marco A. Casanova} } @article {sam2011, title = {A classification for community discovery methods in complex networks}, journal = {Statistical Analysis and Data Mining}, volume = {4}, number = {5}, year = {2011}, pages = {512-546}, author = {Michele Coscia and Fosca Giannotti and Dino Pedreschi} } @article {MonrealeTPRB11, title = {C-safety: a framework for the anonymization of semantic trajectories}, journal = {Transactions on Data Privacy}, volume = {4}, number = {2}, year = {2011}, pages = {73-101}, abstract = {The increasing abundance of data about the trajectories of personal movement is opening new opportunities for analyzing and mining human mobility. However, new risks emerge since it opens new ways of intruding into personal privacy. Representing the personal movements as sequences of places visited by a person during her/his movements - semantic trajectory - poses great privacy threats. In this paper we propose a privacy model defining the attack model of semantic trajectory linking and a privacy notion, called c-safety based on a generalization of visited places based on a taxonomy. This method provides an upper bound to the probability of inferring that a given person, observed in a sequence of non-sensitive places, has also visited any sensitive location. Coherently with the privacy model, we propose an algorithm for transforming any dataset of semantic trajectories into a c-safe one. We report a study on two real-life GPS trajectory datasets to show how our algorithm preserves interesting quality/utility measures of the original trajectories, when mining semantic trajectories sequential pattern mining results. We also empirically measure how the probability that the attacker{\textquoteright}s inference succeeds is much lower than the theoretical upper bound established.}, url = {http://dl.acm.org/citation.cfm?id=2019319\&CFID=803961971\&CFTOKEN=35994039}, author = {Anna Monreale and Roberto Trasarti and Dino Pedreschi and Chiara Renso and Vania Bogorny} } @article {800, title = {Comparison of evolutionary algorithms in gene regulatory network model inference.}, journal = {BMC Bioinformatics}, volume = {11}, year = {2010}, month = {2010}, pages = {59}, abstract = {

BACKGROUND: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient.

RESULTS: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared.

CONCLUSIONS: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.

}, issn = {1471-2105}, doi = {10.1186/1471-2105-11-59}, author = {Alina Sirbu and Heather J Ruskin and Martin Crane} } @article {799, title = {Cross-platform microarray data normalisation for regulatory network inference.}, journal = {PLoS One}, volume = {5}, year = {2010}, month = {2010}, pages = {e13822}, abstract = {

BACKGROUND: Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences.

METHODS: We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets.

CONCLUSIONS: Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0013822}, author = {Alina Sirbu and Heather J Ruskin and Martin Crane} } @article {BonchiGLOPT09, title = {A constraint-based querying system for exploratory pattern discovery}, journal = {Inf. Syst.}, volume = {34}, number = {1}, year = {2009}, pages = {3-27}, author = {Francesco Bonchi and Fosca Giannotti and Claudio Lucchese and Salvatore Orlando and Raffaele Perego and Roberto Trasarti} } @article {BonchiGLOPT09, title = {A constraint-based querying system for exploratory pattern discovery}, journal = {Inf. Syst.}, volume = {34}, number = {1}, year = {2009}, pages = {3-27}, author = {Francesco Bonchi and Fosca Giannotti and Claudio Lucchese and Salvatore Orlando and Raffaele Perego and Roberto Trasarti} } @conference {GrossiRR08, title = {A Case Study in Sequential Pattern Mining for IT-Operational Risk}, booktitle = {ECML/PKDD (1)}, year = {2008}, pages = {424-439}, author = {Valerio Grossi and Andrea Romei and Salvatore Ruggieri} } @inbook {630, title = {Characterising the Next Generation of Mobile Applications Through a Privacy-Aware Geographic Knowledge Discovery Process}, booktitle = {Mobility, Data Mining and Privacy}, year = {2008}, pages = {39-72}, publisher = {a Knowledge Discovery vision}, organization = {a Knowledge Discovery vision}, address = {Mobility, Privacy, and Geography}, author = {Monica Wachowicz and Arend Ligtenberg and Chiara Renso and Seda F. 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Log. Program.}, volume = {28}, number = {2}, year = {1996}, pages = {147-180}, author = {Krzysztof R. Apt and Maurizio Gabbrielli and Dino Pedreschi} } @conference {DBLP:conf/agp/PedreschiR95, title = {A Case Study in Logic Program Verification: the Vanilla Metainterpreter}, booktitle = {GULP-PRODE}, year = {1995}, pages = {643-654}, author = {Dino Pedreschi and Salvatore Ruggieri} } @conference {DBLP:conf/sebd/AmatoGM94, title = {Conservative Multigranularity Locking for an Obiect-Oriented Persistent Language via Abstract Interpretation}, booktitle = {SEBD}, year = {1994}, pages = {329-349}, author = {Giuseppe Amato and Fosca Giannotti and Gianni Mainetto} } @article {DBLP:journals/jlp/MancarellaMP88, title = {Complete Logic Programs with Domain-Closure Axiom}, journal = {J. Log. Program.}, volume = {5}, number = {3}, year = {1988}, pages = {263-276}, author = {Paolo Mancarella and Simone Martini and Dino Pedreschi} }