by Amr El Abbadi (UC Santa Barbara)

Short Bio:
Amr El Abbadi is currently a Professor in the Computer Science Department at the University of California, Santa Barbara. He received his B. Eng. in Computer Science from Alexandria University, Egypt, and received his Ph.D. in Computer Science from Cornell University in August 1987. Prof. El Abbadi is an ACM Fellow. He has served as a journal editor for several database journals, including, currently, The VLDB Journal. He has been Program Chair for multiple database and distributed systems conferences, most recently SIGSPATIAL GIS 2010 and ACM Symposium on Cloud Computing (SoCC) 2011. He has also served as a board member of the VLDB Endowment from 2002—2008. In 2007, Prof. El Abbadi received the UCSB Senate Outstanding Mentorship Award for his excellence in mentoring graduate students. He has published over 250 articles in databases and distributed systems.

With hundreds of millions of users worldwide, social networks provide great opportunities for social connection, learning, political and social change, as well as individual entertainment and enhancement in a wide variety of forms. Because many social interactions currently take place in online networks, social scientists have access to unprecedented amounts of information about social interaction. Prior to online networks, these investigations required resource-intensive activities such as random trials, surveys, and manual data collection to gather even small data sets. Now, massive amounts of information about social networks and social interactions are recorded. This wealth of data can allow social scientists to study social interactions on a scale and at a level of detail that has never before been possible. In addition to providing a platform for scientists to observe social interactions in large scale, online social networks are also changing the very nature of social interactions. People now have ready access to almost inconceivably vast information repositories that are increasingly portable, accessible, and interactive in both delivery and formation. Basic human activities have changed as a result, and new possibilities have emerged. For instance, the process by which people locate, organize, and coordinate groups of individuals with shared interests, the number and nature of information and news sources available, and the ability to solicit and share opinions and ideas across various topics have all undergone dramatic change with the rise of social networks.

Social networks have already emerged as a significant medium for the widespread distribution of news and instructions in mass convergence events such as the 2008 U.S. Presidential Election, the 2009 Presidential election in Iran, and emergencies like the landfall of Hurricanes Ike and Gustav in the fall of 2008. Use of social networks such as Facebook and Twitter has also been noted as providing great ease during the recent demonstrations in Middle East. In light of these notable outcomes, understanding information diffusion over online social networks is a critical research goal. This greater understanding can be achieved through data analysis, the development of reliable models that can predict outcomes of social processes, and ultimately the creation of applications that can shape the outcome of these processes. In this tutorial, we aim to provide an overview of such recent research based on a wide variety of techniques such as optimization algorithms, data mining, data streams covering a large number of problems such as influence spread maximization, misinformation limitation and study of trends in online social networks.


by Markus Schneider (University of Florida)

Short Bio:
Markus Schneider is an Associate Professor at the Department of Computer and Information Science and Engineering of the University of Florida, which is located in Gainesville, Florida, USA. He holds an M.S. degree in Computer Science from the Technical University in Dortmund, Germany, and a Ph.D. degree in Computer Science from the University of Hagen, Germany. His research interests include spatial, spatio-temporal, and moving objects databases, spatial data warehousing and SOLAP, spatial information science, geoinformatics, geographical information systems, applied computational geometry, and extensible databases. He is the co-author of the bookMoving Objects Databases published by Morgan-Kaufmann, the author of the book Spatial Data Types for Database Systems published by Springer-Verlag, and the author of the book Implementation Concepts for Database Systems published by Springer-Verlag. Further, he has published more than 100 journal articles, book chapters, and conference papers. He is on the editorial board of the journal GeoInformatica and a recipient of the 2004 National Science Foundation (NSF) CAREER Award. More details can be found at http://www.cise.ufl.edu/~mschneid.

Spatial databases are full-fledged databases that, in addition, enable the storage, retrieval, manipulation, querying, and analysis of geometries like points, lines, and regions representing, for example, the geometries of cities, roads, and states respectively. More complex examples are spatial partitions representing spatial subdivisions like the counties in Florida and the election districts in Gainesville, and spatial graphs representing spatial networks like transportation networks and pipeline systems. Moving objects databases also deal with geometries but focus on the change of their location and/or shape and/or extent over time. Examples are moving points representing cell phone users, moving lines representing traffic congestions, and moving regions representing hurricanes. The objective of this tutorial is to highlight the state of the art of spatial and moving objects databases, and indicate their future research challenges. The focus is on data models, querying, data structures, algorithms, and system architectures. The tutorial will show that spatial data types and spatiotemporal data types provide a fundamental abstraction for modeling the geometric structure of objects in space, the temporally evolving structure of objects in space and time, their relationships, properties, and operations. These data types develop their full expressive power if they are integrated as abstract data types into databases and query languages.


by Ana Paula Appel (IBM Research Brazil) e Estevam R. Hruschka Jr. (UFSCar)

This tutorial will be presented in Portuguese by Ana Paula Appel.

Tutorial Presentation - Anna Paula Appel

Short Bio:
Ana Paula Appel received her B.Sc. in Computer Science from Federal University of São Carlos (2000), her M.Sc. and Ph.D. degrees in Computer Science from State University of São Paulo in 2003 and 2010, respectively. She got a postdoc at Federal University of São Carlos in 2011. Also, she had an internship at Carnegie Mellon Univeristy - EUA under guidance of Professor Christos Faloutsos in 2008 and was assistant professor at Federal Univeristy of Espírito Santo at Centro Universitário Norte do Espírito Santo (2010-2012), where she worked with database and data mining for a year. She is currently a researcher staff member at IBM Research Brazil, where she leads the data mining PIC group and the graph mining interesting group. She has publication in data mining and complex network at conferences such as TKDD, PKDD, SIAM SDM, ADBIS e SBBD. She is also part of program committee at ASONAM (ACM/IEEE) e PKDD.

Estevam R. Hruschka Jr. received his B.Sc. and M.Sc. degrees in Computer Science from State University of Londrina, Brazil, and from University of Brasilia, Brazil, in 1994 and 1997, respectively, and his Ph.D. degree in Computational Systems from Federal University of Rio de Janeiro, Brazil, 2003.  He has been "young research fellow" at FAPESP (Sao Paulo state research agency) and, currently, he is "research fellow" at CNPq (Brazilian research agency). Also, he is assistant professor at Federal University of São Carlos (UFSCar), Brazil and visiting/collaborating professor at Carnegie Mellon University (Pittsburgh, USA). His main research interests are machine learning, probabilistic graphical models, Bayesina models, evolutionary computation and graph theory. Professor Estevam is also member of the editorial board of "Intelligent Data Analysis - IOS Press" journal and "Advances in Distributed Computing and Artificial Intelligence Journal (ADCAIJ)". He has worked with many international research teams collaborating with research groups from universities such as Carnegie Mellon University (EUA), Stanford University (EUA), University of Washington (EUA), Oregon State university (EUA), University of Waterloo (CAN), and also from companies such as Google Inc. (EUA), Yahoo! Inc. (EUA), BBN Inc. (EUA), CYC Corp. (EUA), E-BIZ Solutions (Brazil).

A key challenge for data mining is tackling the problem of mining richly structured, heterogeneous datasets, which are better represented by structures called graphs. Graphs are convenient representation for a large set of data, as complex network, social network, publication network, and so on. In the last decade the volume of data represented as graphs (as LinkedIn, Facebook, Flickr) have increased exponentially attracting the interest of numerous research groups. This new approach has been driven largely by the availability of computers and communication networks that allow us to gather and analyze data on a scale far larger than previously possible. In this tutorial, we will focus on the following topics: (i) Overview about complex network and description of some properties such as power law degree distribution, cluster coefficient, and small world effect; (ii) Overview about link prediction and the main used algorithms in this field; and (iii) Description of real applications that use or can use link prediction (social networks and some complex systems).