Amr El Abbadi - UC Santa Barbara
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.
Delivering the Promise of Scalable and Elastic Data Management in the Cloud
Over the past two decades, database and systems researchers have made significant advances in the development of algorithms and techniques to provide data management solutions that carefully balance the three major requirements when dealing with critical data: high availability, reliability, and data consistency. However, over the past few years the data requirements, in terms of data availability and system scalability, from Internet scale enterprises that provide services and cater to millions of users has been unprecedented. Cloud computing has emerged as an extremely successful paradigm for deploying Internet and Web-based applications. Scalability, elasticity, pay-per-use pricing, and autonomic control of large-scale operations are the major reasons for the successful widespread adoption of cloud infrastructures. In this talk, we analyze the design choices that allowed modern scalable data management systems to achieve orders of magnitude higher levels of scalability compared to traditional databases. With this understanding, we highlight some design principles for data management systems that can be used to augment existing databases with new cloud features such as scalability, elasticity, and autonomy. We then analyze several state of the art systems and discuss our proposed system, G-Store, which provides transactional guarantees on data granules formed on-demand while being efficient and scalable. Finally, we will present Zephyr, a technique for on-demand live database migration, which is critical to provide lightweight elasticity as a first class notion in the next generation of database systems. Zephyr efficiently migrates live databases in a shared nothing transactional database architecture.
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.
"Complex Object Management in Databases: About the Preparedness of Database Technology for New Emerging Applications".
The quantity and nature of data has changed over the years. While the beginning of database technology was characterized by the handling of manageable volumes of alphanumerical data of simple structure, we are now confronted with "big data". The phrase "big data" refers to large, diverse, complex, and/or distributed
data sets. Large data sets are generated, for example, from instruments, sensors, and satellites and usually have a simple internal structure. The focus of this talk is on the aspect of the complexity of big data. New emerging (that is, non-traditional) applications including biological, genomic, multimedia, digital library, imaging, scientific, location-based, geospatial, and spatiotemporal technologies have necessitated the handling of complex application objects. These objects are highly structured, large in size, and of variable representation length. Currently, such objects are handled by using scientific file formats like HDF and NetCDF, or by special, built-in data types in databases like XML and BLOB.
However, some of these approaches are very application specific and/or do not provide proper levels of data abstraction for users. Others do not support random updates or cannot manage large volumes of structured data and simultaneously provide associated high-level operations. In this talk, we consider the state of the art of complex object management in databases and analyze the requirements, solutions, and weaknesses of available approaches. Finally, we introduce our ongoing work on a novel two-step solution to managing and querying complex application objects within databases. The first step introduces a novel data type called Intelligent Binary Large Object (iBLOB) that leverages the traditional BLOB type in databases, preserves the structure of application objects, and provides smart query and update capabilities. The second step consists in a generalized conceptual framework to capture and validate the structure of application objects by means of a type structure specification (TSS). The iBLOB framework generates a type structure specific application programming interface that allows applications to easily access the components of complex application objects. This greatly simplifies the ease with which new type systems for complex application objects can be implemented inside database systems.
Altigran Soares da Silva
Altigran Soares da Silva is an associate professor at the Institute of Computer Science, Federal University of Amazonas (IComp / UFAM) where he works as a researcher, lecturer and advisor at the undergraduate, master's and doctoral degrees. Received his Ph.D. in Computer Science from Universidade Federal de Minas Gerais (UFMG) in 2002. His research interests involve Data Management, Information Retrieval and Data Mining with emphasis on the World-Wide Web environment. On these subjects, he has coordinated and participated in dozens of research projects that resulted in more than 100 scientific publications in journals and conference proceedings of high quality in these areas. In 2007 he was the Program Committee Chair of the Brazilian Symposium on Databases (SBBD) and in 2010 he worked as a co-chair in "Bridging Structured and Unstructured Data" track of the International World Wide Web Conference. He also participated as a member of technical program committees in some 40 conferences and workshops in Brazil and abroad. In 2012, he was appointed as an Invited Speaker for SBBD. He served between 2007 and 2009 as the Pro-rector for Research and Graduate Studies at UFAM. He is currently the Assistant Coordinator of the Computer Science area at CAPES (Brazil's National Agency for Graduate Programs) and, since 2005, he is a board member of the Brazilian Computer Society (SBC). It is co-founder of technology ventures, including the Akwan Information Technologies, acquired by Google Inc. in 2005
Exploring Structured Data in Textual Content from the Web: Methods, Techniques and Applications
Although search engines are currently the most effective and popular tools for Information Retrieval on the Web, there is now a consensus that it is still possible to exploit more effectively the potential of these systems. This is particularly true in the current scenario of expansion of social networks, consolidation of the Web 2.0, and emergence of the so called Web of Data. This finding led to the emergence of multiple proposals to increase the expressive power of queries over Web content, both from the syntactical point of view, for example, by the adoption of the XML technology, and from the semantic point of view, for example, through the adoption of the resources collectively known as the Semantic Web.
Although very promising, some of these proposals have run into a difficulty in the adoption of standards, which is an inherent characteristic of the nature of the Web. In this talk we focus on another possible perspective to address this issue: the development of methods and techniques for automatically gathering, extracting and exploiting (semi) structured data that are implicitly available in the vast unstructured textual content on the Web. Works that seek to effectively exploit these data have appeared in the literature for over a decade. However, a series of recent advances in Information Retrieval, Machine Learning and Data Mining, gave this issue a new impulse on the scientific community. This can be evidenced by the considerable space that venues of important areas such as Databases, Information Retrieval and Artificial Intelligence have devoted to research work related to it. Such an interest is justified not only by the challenging problems that arise, but mainly by the growing demand from industry to solve these problems. This makes the results of research on this subject not only immediately applicable, but also motivate a continuous feedback for the scientific investigation around it. The theme involves several classes of problems, and some of these classes of problems will be addressed here, namely: Data Extraction from Textual Sources, Focused Crawling of Web Pages, Integration of Data available in Textual Web Sources and Web Search Considering Structural Features.