by Amr El Abbadi (UC Santa Barbara)

Esse tutorial será proferido em inglês.

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)

Esse tutorial será proferido em inglês.

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)

Esse tutorial será proferido em português por Ana Paula Appel.

Slides do Tutorial - Anna Paula Appel

Short Bio:
Ana Paula Appel cursou Bacharelado em Ciência da Computação pela Universidade Federal de São Carlos (2000), Mestrado e Doutorado em Ciência da Computação pela Universidade de São Paulo (2003 e 2010), e pós-doutorado pela Universidade Federal de São Carlos (2011). Durante o doutorado, realizou estágio sanduíche na Carnegie Mellon Univeristy - EUA sobre supervisão do Prof. Dr. Christos Faloutsos (2008). Ela foi professora adjunta da Universidade Federal do Espírito Santo no Centro Universitário Norte do Espírito Santo na área de banco de dados (2010-2012). Atualmente, é pesquisadora da IBM Research Brasil, liderando o PIC de mineração de dados e o grupo de mineração de redes complexas. Ana Paula Appel vem atuando na área relacionada a mineração de redes complexas por 1 ano. Possui publicações na área de mineração de dados e redes complexas em conferências como TKDD, PKDD, SIAM SDM, ADBIS e SBBD. Faz parte do comitê da conferência ASONAM (ACM/IEEE) e PKDD.

Estevam R. Hruschka Jr. possui graduação em Ciências da Computação pela Universidade Estadual de Londrina (1994), mestrado em Ciência da Computação pela Universidade de Brasília (1997) e doutorado em Sistemas Computacionais de Alto Desempenho pela Universidade Federal do Rio de Janeiro (PEC/COPPE/UFRJ) (2003). Foi jovem pesquisador FAPESP e atualmente é pesquisador CNPq (PQ-2), pesquisador e professor adjunto da Universidade Federal de São Carlos e professor colaborador/visitante na Carnegie Mellon University (Pittsburgh, EUA). Tem experiência na área de Ciência da Computação, com ênfase em Aprendizado de Máquina, Mineração de Dados. Atuando principalmente nos seguintes temas: aprendizado de máquina, aprendizado sem fim, modelos gráficos probabilísticos, modelos Bayesianos, algoritmos evolutivos e teoria dos grafos. É membro do comitê editorial do periódico "Intelligent Data Analysis - IOS Press" e "Advances in Distributed Computing and Artificial Intelligence Journal (ADCAIJ)". Tem desenvolvido projetos de cooperação científica internacional com universidades como Carnegie Mellon University (EUA), Stanford University (EUA), University of Washington (EUA), Oregon State university (EUA), e University of Waterloo (CAN), além de parcerias e colaboração com empresas nacionais e multinacionais como Google Inc. (EUA), Yahoo! Inc. (EUA), BBN Inc. (EUA), CYC Corp. (EUA), E-BIZ Solutions (Brasil). Tem trabalhado como revisor para periódicos científicos relevantes, assim como também participado do comitê de programa de várias conferências científicas nacionais e internacionais.

Um dos grandes desafios presente nas pesquisas em mineração de dados está relacionado com o problema da mineração de bases de dados que são melhor representadas por estruturas chamadas redes complexas modeladas por grafos. Grafos são representações convenientes para um grande conjunto de tipos de dados, como redes sociais, redes de informações, patentes, entre outros. Na última década, o volume de dados representados como grafos (como LinkedIn, Facebook, Flickr, etc.) tem aumentado exponencialmente, atraindo o interesse de inúmeros grupos de pesquisa em investigar aspectos relacionados a este novo tipo de dado e os impactos de sua utilização em tarefas de mineração de dados. Esta nova abordagem tem sido impulsionada principalmente pela disponibilidade de computadores e redes de comunicação que nos permitem recolher e analisar dados em uma escala muito maior do que era possível anteriormente. Neste tutorial pesquisas que envolvem aspectos relacionados a redes complexas serão abordadas de maneira a permitir ao expectador conhecer conceitos básicos, métodos tradicionais e modernos. Serão descritas também técnicas computacionais que permitem a manipulação computacional eficiente de redes complexas, bem como domínios de aplicação e exemplos práticos e reais.