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Video της ομιλίας (~2.3GB): www.intelligence.tuc.gr/~lagoudakis/SHARE/2012-01-26-TUCECE-Talk-Ailamaki.mpg

Ομιλητής: Αναστασία Αϊλαμάκη, École Polytechnique Fédérale de Lausanne, Ελβετία (http://people.epfl.ch/anastasia.ailamaki)
Ημερομηνία: Πέμπτη 26 Ιανουαρίου 2012
Ώρα: 10:00πμ
Αίθουσα: Αμφιθέατρο Κτιρίου Επιστημών

Τίτλος

From 10,000 to 1,000,000 neurons with an index

Περίληψη

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.

Βιογραφικό

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.

Video της ομιλίας (~1.9GB): www.intelligence.tuc.gr/~lagoudakis/SHARE/2012-01-09-TUCECE-Talk-Rekatsinas.mpg

Ομιλητής: Θεόδωρος Ρεκατσίνας, University of Maryland (www.cs.umd.edu/~thodrek)
Ημερομηνία: Δευτέρα 9 Ιανουαρίου 2012
Ώρα: 12:00μμ
Αίθουσα: Αμφιθέατρο Κτιρίου Επιστημών

Title

Exploiting Local Structure in Correlated Probabilistic Databases

Abstract

Increasing numbers of real-world application domains are generating data that is inherently noisy, incomplete, and probabilistic in nature. Statistical analysis and probabilistic inference, widely used in those domains often introduce additional layers of uncertainty. Examples include data integration and information extraction on the Web, social network analysis and biomedical data management. Managing and queering such data requires us to combine the tools and the techniques from a variety of disciplines including databases, first-order logic, and probabilistic reasoning. Over the last few years, numerous approaches have been proposed, and several systems built, to integrate uncertainty into databases. Most approaches make restrictive independence assumptions concerning the types of uncertainties that can be represented and cannot easily model rich levels of abstractions, needed to handle large-scale uncertain datasets.

In this talk, I will begin by presenting the probabilistic data management system developed at the University of Maryland, called PrDB, aimed at supporting rich correlation structures often present in real-world uncertain datasets. I will present the PrDB representation model, which is based on probabilistic graphical models and show how these enable PrDB to support uncertainties specified at various abstraction levels, from schema-level uncertainties to tuple-specific uncertainties. Query evaluation in PrDB can be seen as equivalent to inference in graphical models, and I will present a novel technique we developed for efficient query evaluation and its relationship to factorized representations of probability distributions, such as Arithmetic Circuits and Ordered Binary Decision Diagrams. I will conclude with some of the open research challenges in the area.

Bio

Thodoris Rekatsinas is a second year Ph.D. student in Computer Science at the University of Maryland. He is a member of the DB Group, working with Prof. Amol Deshpande and a member of the LINQS Laboratory directed by Prof. Lise Getoor. His research interests are in the fields of machine learning, large scale probabilistic inference, probabilistic and temporal graph databases. Prior to joining UMD, he received his Diploma in ECE from the National Technical University of Athens.

Μπορείτε να βρείτε τις διαφάνειες (PDF), το video (MPG) και μερικές φωτογραφίες (JPG) από την προχθεσινή εκδήλωση του Τμήματος για τις μεταπτυχιακές σπουδές στον ακόλουθο σύνδεσμο:

http://www.intelligence.tuc.gr/~lagoudakis/SHARE/2011-12-09-TUCECE-Talk-GraduateStudies/

Προσοχή: το video είναι 2.1GB και διαρκεί περίπου 2 ώρες! Αν τυχόν δεν το κάνει αυτόματα ο player που χρησιμοποιείτε, επιλέξτε Video Aspect Ratio = 16:9 για να φαίνεται σωστά η εικόνα.

Την ερχόμενη Παρασκευή, 9 Δεκεμβρίου, στις 18:00, στην αίθουσα 145Π42, θα δοθεί ομιλία με θέμα: "Μεταπτυχιακές Σπουδές στο εξωτερικό και στην Ελλάδα". Θα καλυφθούν θέματα όπως: ποιο είναι το προφίλ ενός υποψήφιου μεταπτυχιακού/διδακτορικού φοιτητή, πώς μπορείτε να επιλέξετε πανεπιστήμια/Τμήματα και επιβλέποντες καθηγητές, πώς πρέπει να προετοιμασθείτε και ποια είναι η διαδικασία των αιτήσεων, πώς είναι η ζωή ενός μεταπτυχιακού φοιτητή, ποιες δυνατότητες υπάρχουν στην αγορά εργασίας για έναν απόφοιτο με μεταπτυχιακό.
Ο λόγος που δίνεται η ομιλία είναι ο σημαντικός αριθμός ερωτήσεων που δεχόμαστε από φοιτητές που ενδιαφέρονται για μεταπτυχιακές σπουδές. Η ομιλία απευθύνεται σε φοιτητές από όλα τα έτη, οι οποίοι ενδιαφέρονται να ενημερωθούν για το θέμα.

Εκ μέρους του Τμήματος,
Πολυχρόνης Κουτσάκης

Πρακτική Άσκηση Φοιτητών Τμήματος ΗΜΜΥ Πολυτεχνείου Κρήτης

Παρακαλούνται όσοι φοιτητές * πραγματοποίησαν πρακτική άσκηση κατά τους θερινούς μήνες 2011, να περάσουν από το Λογιστήριο του ΕΛΚΕ (κα Γιαννοπούλου Ευαγγελία – Κτίριο Πρυτανείας – από 09:00 έως και 13:00) να παραλάβουν τις επιταγές τους.

Καθ. Δ. Πνευματικάτος - Β. Γρηγοράκη

Διαχειριστές Προγράμματος Πρακτικής Άσκησης

*Οι φοιτητές με αριθμό μητρώου 2007030027, 2008030069, 2008030019 και 2006030087 θα παραλάβουν τις επιταγές μετά από νέα ανακοίνωση.