Database Queries – Logic and Complexity Moshe Y. Vardi, Rice University Mathematical logic emerged during the early part of the 20 Century, out of a foundational investigation of mathematics, as the basic language of mathematics. In 1970 Codd proposed the relational database model, based on mathematical logic: logical structures offer a way to model data, while logical formulas offer a way to express database queries. This proposal gave rise to a multi-billion dollar relational database industry as well as a rich theory of logical query languages. This talk will offer an overview of how mathematical logic came to provide foundations for one of today's most important technologies, and show how the theory of logical queries offer deep insights into the computational complexity of evaluating relational queries. Moshe Y. Vardi is the George Distinguish Service Professor in Computational Engineering and Director of the Ken Kennedy Institute for Information Technology Institute at Rice University. He is the co-recipient of three IBM Outstanding Innovation Awards, the ACM SIGACT Goedel Prize, the ACM Kanellakis Award, the ACM SIGMOD Codd Award, the Blaise Pascal Medal, and the IEEE Computer Society Goode Award. He is the author and co-author of over 400 papers, as well as two books: Reasoning about Knowledge and Finite Model Theory and Its Applications. He is a Fellow of the Association for Computing Machinery, the American Association for Artificial Intelligence, the American Association for the Advancement of Science, and the Institute for Electrical and Electronic Engineers. He is a member of the US National Academy of Engineering, the American Academy of Arts and Science, the European Academy of Science, and Academia Europea. He holds honorary doctorates from the Saarland University in Germany and Orleans University in France. He is the Editor-in-Chief of the Communications of the ACM. Scientific Data Management: Not your everyday transaction Anastasia Ailamaki, EPFL Lausanne Today's scientific processes heavily depend on fast and accurate analysis of experimental data. Scientists are routinely overwhelmed by the effort needed to manage the volumes of data produced either by observing phenomena or by sophisticated simulations. As database systems have proven inefficient, inadequate, or insufficient to meet the needs of scientific applications, the scientific community typically uses special- purpose legacy software. When compared to a general-purpose data management system, however, application-specific systems require more resources to maintain, and in order to achieve acceptable performance they often sacrifice data independence and hinder the reuse of knowledge. With the exponential growth of dataset sizes, data management technology are no longer luxury; they are the sole solution for scientific applications. I will discuss some of the work from teams around the world and the requirements of their applications, as well as how these translate to challenges for the data management community. As an example I will describe a challenging application on brain simulation data, and its needs; I will then present how we were able to simulate a meaningful percentage of the human brain as well as access arbitrary brain regions fast, independently of increasing data size or density. Finally I will present some of the dat management challenges that lie ahead in domain sciences. Anastasia Ailamaki is a Professor of Computer Sciences at the Ecole Polytechnique Federale de Lausanne (EPFL) in Switzerland. Her research interests are in database systems and applications, and in particular (a) in strengthening the interaction between the database software and emerging hardware and I/O devices, and (b) in automating database management to support computationally-demanding and demanding data- intensive scientific applications. She has received a Finmeccanica endowed chair from the Computer Science Department at Carnegie Mellon (2007), a European Young Investigator Award from the European Science Foundation (2007), an Alfred P. Sloan Research Fellowship (2005), seven best-paper awards at top conferences (2001-2011), and an NSF CAREER award (2002). She earned her Ph.D. in Computer Science from the University of Wisconsin-Madison in 2000. She is a member of IEEE and ACM, and has also been a CRA-W mentor. Open Data François Bancilhon Open Data consists in making available to the general public and to private and public organization PSI (public sector information) for access and reuse. More and more open data is becoming available in most democratic countries following the launch of the data.gov initiative in the US in 2009. The availability of this new information brings a number opportunities and raises a number of challenges. The opportunities are the new applications that companies and organisations can build using this data and the new understanding given to the people who access it. The challenges are the following: most of this data is usually in a poor format (poorly structured xls tables or in some cases even pdf), it is often of poor quality, and it is fragmented in thousands or millions of files with duplicate and/or complementary information. To use these fragmented, poorly structured and poor quality files, several approaches can be used, not necessarily mutually exclusive. One is to move the intelligence from the data into the application and to develop search based applications which directly manages the data as is. Another one is to bring some order in the data using a semantic web approach: converting the data in rdf, identifying entities and linking them from one data set to the other. And a final one is to structure the data by aligning data sets on common attribute and structure, to get closer to a uniform data base scheme. François is currently CEO of Data Publica, a key actor of the Open Data space in France and CEO of the Mobile Services Initiative for INRIA. He has co-founded and/or managed several software startups in France and in the US (Data Publica, Mandriva, Arioso, Xyleme, Ucopia, O2 Technology). Before becoming an entrepreneur, François was a researcher and a university professor, in France and the US, specializing in database technology. François holds an engineering degree from the École des Mines de Paris, a PhD from the University of Michigan and a Doctorate from the University of Paris XI. Web archiving Julien Masanès The Web represents the largest source of open information ever produced in history. Larger than the printed sphere by several order of magnitude, it also exhibit specific characteristics compared to traditional media, such as it's collaborative editing to which a large fraction of humanity participates, even marginally, it's complex dynamics and the paradoxical nature of traces it conveys, both ubiquitous and fragile at the same time. These unique features also led the web to become a major source for modern information, analysis and study, and the capacity to preserve its memory an important issue for the future. But these features also require to lay new methodological and practical foundations in the well-established field of cultural artefacts preservation. This presentation will outline the salient properties of the Web viewed from the somewhat different angle of its preservation and offer some insight into how its memory can be built to serve science in the future. Julien Masanès is Director of the Internet Memory, a non-profit foundation for web preservation and digital cultural access. Before this he directed the Web Archiving Project at the Bibliothèque Nationale de France since 2000. He also actively participated in the creation of the International Internet Preservation Consortium (IIPC), which he has coordinated during the first two years. He contributes in various national and international initiatives and provides advices for the European Commission as an expert in the domain of digital preservation and web archiving. He has also launched and presently chairs the International Web Archiving Workshop (IWAW) series, the main international rendezvous in this field. Julien Masanès studied Philosophy and Cognitive Science, gaining his MS in Philosophy from the Sorbonne in 1992 and his MS in Cognitive Science from the Ecole des Hautes Etudes en Sciences Sociales (EHESS) in 1994. In 2000 he gained a MS in librarianship at the Ecole Nationale Supérieure des Sciences de l'information et des Bibliothèques (ENSSIB). Static Analysis and Verification Victor Vianu, U.C. San Diego Correctness and good performance are essential desiderata for database systems and the many applications relying on databases. Indeed, bugs and performance problems are commonly encountered in such systems and can range from annoying to catastrophic. Static analysis and verification provide tools for automatic reasoning about queries and applications in order to guarantee desirable behavior. Unfortunately, such reasoning, carried out by programs that take as input other programs, quickly runs against fundamental limitations of computing. In the cases when it is feasible, it often requires a sophisticated mix of techniques from logic and automata theory. This talk will discuss some of the challenges and intrinsic limitations of static analysis and verification and identify situations where it can be very effective. Victor Vianu is a Professor of Computer Science at the University of California, San Diego. He received his PhD in Computer Science from the University of Southern California in 1983. He has spent sabbaticals