<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN" "http://www.w3.org/TR/REC-html40/loose.dtd">
<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"><title>BigDat 2017: works in progress February 10</title></head><body>*To be removed from our mailing list, please respond to this message with UNSUBSCRIBE in the subject line*<br>
<br>
********************************************************<br>
<br><strong>3<sup>rd</sup> INTERNATIONAL WINTER SCHOOL ON BIG DATA</strong><br>
<br><strong>BigDat 201</strong><strong>7</strong><br>
<br><strong>Bari</strong><strong>, Italy</strong><br>
<br><strong>February 13-17, 2017</strong><br>
<br>
Organized by:<br>
University of Bari "Aldo Moro"<br>
Rovira i Virgili University<br>
<br>
http://grammars.grlmc.com/BigDat2017/<br>
<br>
********************************************************<br>
<br><u>--- Regular registration deadline: February 10, 2017 ---</u><br>
<br>
********************************************************<br>
<br><strong>AIM:</strong><br>
<br>
BigDat 2017 will be a research training event with a global scope aiming at updating participants about the most recent advances in the critical and fast developing area of big data, which covers a large spectrum of current exciting research and industrial innovation with an extraordinary potential for a huge impact on scientific discoveries, medicine, engineering, business models, and society itself. Renowned academics and industry pioneers will lecture and share their views with the audience.<br>
<br>
Most big data subareas will be displayed, namely foundations, infrastructure, management, search and mining, security and privacy, and applications (to biological and health sciences, to business, finance and transportation, to online social networks, etc.). Main challenges of analytics, management and storage of big data will be identified through 4 keynote lectures, 23 six-hour courses, and 1 round table, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Interaction will be a main component of the event. An open session will give participants the opportunity to present their own work in progress in 5 minutes.<br>
<br><strong>ADDRESSED TO:</strong><br>
<br>
In principle, graduate students, PhD students and postdocs from around the world will be the most typical profiles. However, there are no formal pre-requisites for participation in terms of academic degrees. Since there will be differences in level, specific knowledge background may be assumed for some of the courses. BigDat 2017 is also appropriate for more senior people who want to keep themselves updated on recent developments and future trends. All will surely find it fruitful to listen and discuss with major researchers, industry leaders and innovators.<br>
<br><strong>REGIME:</strong><br>
<br>
In addition to keynotes, 2-3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another.<br>
<br><strong>VENUE:</strong><br>
<br>
BigDat 2017 will take place in Bari, a lively university city on the Adriatic Sea in Southern Italy. The venue will be:<br>
<br>
Department of Computer Science<br>
University of Bari "Aldo Moro"<br>
via Orabona, 4<br>
70125 Bari, Italy<br>
<br><strong>KEYNOTE SPEAKERS:</strong><br>
<br>
Ernesto Damiani (EBTIC, Khalifa University, Abu Dhabi & University of Milan), Model-driven Development of Big Data Applications<br>
<br>
Daniele Quercia (Bell Labs), Good City Life<br>
<br><strong>PROFESSORS AND COURSES:</strong><br>
<br>
Paul Bliese (University of South Carolina), [introductory/intermediate] Using R for Mixed-effects (Multilevel) Models<br>
<br>
Hendrik Blockeel (KU Leuven), [intermediate] Decision Trees for Big Data Analytics<br>
<br>
Tamás Budavári (Johns Hopkins University), [introductory] Big Data Approaches in Astronomy<br>
<br>
Diego Calvanese (Free University of Bozen-Bolzano), [advanced] Data-aware Processes: Modeling and Verification<br>
<br>
Amr El Abbadi (University of California, Santa Barbara), [introductory/intermediate] Managing Big Data in the Cloud<br>
<br>
Geoffrey C. Fox (Indiana University), [intermediate] Using High Performance Computing for Big Data Analytics<br>
<br>
Minos Garofalakis (Technical University of Crete), [intermediate/advanced] Streaming Big Data Analytics<br>
<br>
David W. Gerbing (Portland State University), [introductory] Data Visualization with R<br>
<br>
Georgios B. Giannakis (University of Minnesota), [advanced] Signal Processing Tools for Big Network Data Analytics<br>
<br>
Sander Klous (University of Amsterdam), [introductory] We Are Big Data<br>
<br>
Laks V.S. Lakshmanan (University of British Columbia), [introductory] Analysis of Large Social Networks<br>
<br>
Maurizio Lenzerini (Sapienza University of Rome), [intermediate/advanced] Ontology-based Data Management<br>
<br>
Soumya D. Mohanty (University of Texas Rio Grande Valley), [introductory/intermediate] Swarm Intelligence Methods and Optimization Problems in Big Data Analytics<br>
<br>
Bernhard Pfahringer (University of Waikato), [introductory] Introduction to Data Stream Mining for Big Data<br>
<br>
Krithi Ramamritham (Indian Institute of Technology Bombay), [introductory/intermediate] Harnessing Big Data for Building Smart Things<br>
<br>
Michael Rosenblum (University of Potsdam), [introductory/intermediate] Coupled Oscillators Approach in Time Series Analysis<br>
<br>
Pierangela Samarati (University of Milan), [intermediate] Data Security and Privacy in the Cloud<br>
<br>
V.S. Subrahmanian (University of Maryland), [introductory/intermediate] Big Data and Cybersecurity<br>
<br>
Alexander S. Tuzhilin (New York University), [introductory/intermediate] Recommender Systems and Big Data<br>
<br>
Jeffrey Ullman (Stanford University), [introductory] Big Data Algorithms that Aren't Machine Learning<br>
<br>
Lyle Ungar (University of Pennsylvania), [introductory] Sentiment Mining from User Generated Content<br>
<br>
John Wright (Columbia University), [intermediate/advanced] Sparse and Low-Dimensional Models for High-Dimensional Data: Theory, Algorithms and Applications<br>
<br>
Zhongfei Zhang (Binghamton University), [introductory/advanced] Knowledge Discovery from Relational and Multimedia Data<br>
<br><strong>OPEN SESSION</strong><br>
<br>
An open session will collect 5-minute presentations of work in progress by participants. They should submit a half-page abstract containing title, authors, and summary of the research to david.silva409 (at) yahoo.com by February 10, 2017.<br>
<br><strong>ORGANIZING COMMITTEE:</strong><br>
<br>
Annalisa Appice<br>
Michelangelo Ceci (co-chair)<br>
Stefano Franco<br>
Corrado Loglisci<br>
Donato Malerba (co-chair)<br>
Carlos Martín-Vide (co-chair)<br>
Manuel Jesús Parra Royón<br>
Gianvito Pio<br>
David Silva<br>
<br><strong>REGISTRATION:</strong><br>
<br>
It has to be done at<br>
<br>
http://grammars.grlmc.com/BigDat2017/registration.php<br>
<br>
The selection of up to 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an approximation of the respective demand for each course.<br>
<br>
Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration facility disabled when the capacity of the venue will be complete. It is much recommended to register prior to the event.<br>
<br><strong>FEES:</strong><br>
<br>
Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline.<br>
<br><strong>ACCOMMODATION:</strong><br>
<br>
Suggestions for accommodation are available on the website.<br>
<br><strong>CERTIFICATE:</strong><br>
<br>
Participants will be delivered a certificate of attendance.<br>
<br><strong>QUESTIONS AND FURTHER INFORMATION:</strong><br>
<br>
david.silva409 (at) yahoo.com<br>
<br><strong>ACKNOWLEDGMENTS:</strong><br>
<br>
Università degli Studi di Bari Aldo Moro<br>
Universitat Rovira i Virgili</body></html>