Τίτλος: From 10,000 to 1,000,000 neurons with an index
Αναστασία Αϊλαμάκη, École Polytechnique Fédérale de Lausanne, Ελβετία
Ημερομηνία/Ώρα: Πέμπτη 26 Ιανουαρίου 2012, 10:00πμ
Αίθουσα: Αμφιθέατρο Κτιρίου Επιστημών
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 DBMS, 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, with a novel indexing algorithm and some other techniques, 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 challenges that lie ahead in the brain simulation and in other applications.
Anastasia (Natassa) 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.