@conference {1291,
	title = {Weak nodes detection in urban transport systems: Planning for resilience in Singapore},
	booktitle = {2018 IEEE 5th international conference on data science and advanced analytics (DSAA)},
	year = {2018},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g., floods, terrorist attacks, etc. . . ). In this perspective, we propose ACHILLES, an application to models people{\textquoteright}s movements in a given transport mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones, and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention.},
	doi = {10.1109/DSAA.2018.00061},
	url = {https://ieeexplore.ieee.org/abstract/document/8631413/authors$\#$authors},
	author = {Ferretti, Michele and Barlacchi, Gianni and Luca Pappalardo and Lucchini, Lorenzo and Lepri, Bruno}
}
@inbook {818,
	title = {Where Is My Next Friend? Recommending Enjoyable Profiles in Location Based Services},
	booktitle = {Complex Networks VII},
	year = {2016},
	pages = {65{\textendash}78},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {How many of your friends, with whom you enjoy spending some time, live close by? How many people are at your reach, with whom you could have a nice conversation? We introduce a measure of enjoyability that may be the basis for a new class of location-based services aimed at maximizing the likelihood that two persons, or a group of people, would enjoy spending time together. Our enjoyability takes into account both topic similarity between two users and the users{\textquoteright} tendency to connect to people with similar or dissimilar interest. We computed the enjoyability on two datasets of geo-located tweets, and we reasoned on the applicability of the obtained results for producing friend recommendations. We aim at suggesting couples of users which are not friends yet, but which are frequently co-located and maximize our enjoyability measure. By taking into account the spatial dimension, we show how 50 \% of users may find at least one enjoyable person within 10 km of their two most visited locations. Our results are encouraging, and open the way for a new class of recommender systems based on enjoyability.},
	doi = {10.1007/978-3-319-30569-1_5},
	author = {Riccardo Guidotti and Michele Berlingerio}
}
@conference {daytag2013,
	title = {Where Have You Been Today? Annotating Trajectories with DayTag},
	booktitle = {International Conference on Spatial and Spatio-temporal Databases (SSTD)},
	year = {2013},
	pages = {467-471},
	doi = {http://dx.doi.org/10.1007/978-3-642-40235-7_30},
	author = {S Rinzivillo and Fernando de Lucca Siqueira and Lorenzo Gabrielli and Chiara Renso and Vania Bogorny}
}
@conference {540,
	title = {Where Shall We Go Today? Planning Touristic Tours with TripBuilder},
	booktitle = {International Conference CIKM 2013},
	year = {2013},
	address = {San Francisco, USA},
	author = {Igo Brilhante and Franco Maria Nardini and Raffaele Perego and Chiara Renso and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do}
}
@inbook {476,
	title = {What else can be extracted from ontologies? Influence Rules},
	booktitle = {Software and Data Technologies},
	series = {Communications in Computer and Information Science},
	year = {2012},
	publisher = {Springer},
	organization = {Springer},
	author = {Franco Turini and Barbara Furletti}
}
@article {1273,
	title = {Wine and Food Tourism First European Conference},
	journal = {Edizioni ETS Pisa},
	year = {2012},
	author = {Romano, Maria Francesca and Michela Natilli}
}
@article {797,
	title = {Wisdom of crowds for robust gene network inference.},
	journal = {Nat Methods},
	volume = {9},
	year = {2012},
	month = {2012 Aug},
	pages = {796-804},
	abstract = {<p>Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50\%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43\%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.</p>},
	issn = {1548-7105},
	doi = {10.1038/nmeth.2016},
	author = {Daniel Marbach and J.C. Costello and Robert K{\"u}ffner and N.M. Vega and R.J. Prill and D.M. Camacho and K.R. Allison and Manolis Kellis and J.J. Collins and Gustavo Stolovitzky}
}
@article {814,
	title = {Wisdom of crowds for robust gene network inference},
	journal = {Nature Methods},
	volume = {9},
	number = {8},
	year = {2012},
	pages = {796-804},
	doi = {10.1038/nmeth.2016},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870305264\&partnerID=40\&md5=04a686572bdefff60157bf68c95df7ea},
	author = {Daniel Marbach and J.C. Costello and Robert K{\"u}ffner and N.M. Vega and R.J. Prill and D.M. Camacho and K.R. Allison and Manolis Kellis and J.J. Collins and Aderhold, A. and Gustavo Stolovitzky and Bonneau, R. and Chen, Y. and Cordero, F. and Martin Crane and Dondelinger, F. and Drton, M. and Esposito, R. and Foygel, R. and De La Fuente, A. and Gertheiss, J. and Geurts, P. and Greenfield, A. and Grzegorczyk, M. and Haury, A.-C. and Holmes, B. and Hothorn, T. and Husmeier, D. and Huynh-Thu, V.A. and Irrthum, A. and Karlebach, G. and Lebre, S. and De Leo, V. and Madar, A. and Mani, S. and Mordelet, F. and Ostrer, H. and Ouyang, Z. and Pandya, R. and Petri, T. and Pinna, A. and Poultney, C.S. and Rezny, S. and Heather J Ruskin and Saeys, Y. and Shamir, R. and Alina Sirbu and Song, M. and Soranzo, N. and Statnikov, A. and N.M. Vega and Vera-Licona, P. and Vert, J.-P. and Visconti, A. and Haizhou Wang and Wehenkel, L. and Windhager, L. and Zhang, Y. and Zimmer, R.}
}
@conference {RinzivilloR11,
	title = {Who/Where Are My New Customers?},
	booktitle = {ISMIS Industrial Session},
	year = {2011},
	pages = {307-317},
	author = {S Rinzivillo and Salvatore Ruggieri}
}
@proceedings {243,
	title = {WhereNext: a Location Predictor on Trajectory Pattern Mining},
	year = {2009},
	abstract = {The pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data.This side eect provides the opportunity for innovative methods that analyse the behaviors of movements. In this paper we propose WhereNext, which is a method aimed at predicting with a certain level of accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Patterns, which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with a typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. The tree is learned from the Trajectory Patterns that hold a certain area and it may be used as a predictor of the next location of a new trajectory finding the best matching path in the tree. Three dierent best matching methods to classify a new moving object are proposed and their impact on the quality of prediction is studied extensively. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends on the movement of all available objects in a certain area instead of on the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties that have emerged from the data and this allows us to define matching methods that striclty depend on the properties of such movements. In addition, we propose a set of other measures, that evaluate a priori the predictive power of a set of Trajectory Patterns. This measures were tuned on a real life case study. Finally, an exhaustive set of experiments and results on the real dataset are presented.},
	doi = {10.1145/1557019.1557091},
	author = {Anna Monreale and Fabio Pinelli and Roberto Trasarti and Fosca Giannotti}
}
@article {1270,
	title = {Wine tourism in Italy: New profiles, styles of consumption, ways of touring},
	journal = {Turizam: me{\dj}unarodni znanstveno-stru{\v c}ni {\v c}asopis},
	volume = {57},
	number = {4},
	year = {2009},
	pages = {463{\textendash}475},
	author = {Romano, Maria Francesca and Michela Natilli}
}
@inbook {629,
	title = {Wireless Network Data Sources: Tracking and Synthesizing Trajectories},
	booktitle = {Mobility, Data Mining and Privacy},
	year = {2008},
	pages = {73-100},
	author = {Chiara Renso and Simone Puntoni and E. Frentzos and Andrea Mazzoni and Bart Moelans and Nikos Pelekis and F. Pini}
}
@conference {DBLP:conf/sebd/GiannottiNPS03,
	title = {WebCat: Automatic Categorization of Web Search Results},
	booktitle = {SEBD},
	year = {2003},
	pages = {507-518},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and F. Samaritani}
}
@article {BGGMNPRR01,
	title = {Web Log Data Warehousing and Mining for Intelligent Web Caching},
	journal = {Data and Knowledge Engineering},
	year = {2001},
	note = {39:165, November .},
	author = {Francesco Bonchi and Fosca Giannotti and Cristian Gozzi and Giuseppe Manco and Mirco Nanni and Dino Pedreschi and Chiara Renso and Salvatore Ruggieri}
}
@article {DBLP:journals/dke/BonchiGGMNPRR01,
	title = {Web log data warehousing and mining for intelligent web caching},
	journal = {Data Knowl. Eng.},
	volume = {39},
	number = {2},
	year = {2001},
	pages = {165-189},
	author = {Francesco Bonchi and Fosca Giannotti and Cristian Gozzi and Giuseppe Manco and Mirco Nanni and Dino Pedreschi and Chiara Renso and Salvatore Ruggieri}
}
@article {DBLP:journals/dke/BonchiGGMNPRR01,
	title = {Web log data warehousing and mining for intelligent web caching},
	journal = {Data Knowl. Eng.},
	volume = {39},
	number = {2},
	year = {2001},
	pages = {165-189},
	author = {Francesco Bonchi and Fosca Giannotti and Cristian Gozzi and Giuseppe Manco and Mirco Nanni and Dino Pedreschi and Chiara Renso and Salvatore Ruggieri}
}
@article {DBLP:journals/ipl/PedreschiR98,
	title = {Weakest Preconditions for Pure Prolog Programs},
	journal = {Inf. Process. Lett.},
	volume = {67},
	number = {3},
	year = {1998},
	pages = {145-150},
	author = {Dino Pedreschi and Salvatore Ruggieri}
}
@conference {DBLP:conf/agp/ChiarelliMR93,
	title = {A WAM Estesa per la Composizione di Programi Logici},
	booktitle = {GULP},
	year = {1993},
	pages = {189-202},
	author = {A. Chiarelli and V. Mazzotta and Chiara Renso}
}
