February 2013


Options: Use Monospaced Font
Show Text Part by Default
Show All Mail Headers

Message: [<< First] [< Prev] [Next >] [Last >>]
Topic: [<< First] [< Prev] [Next >] [Last >>]
Author: [<< First] [< Prev] [Next >] [Last >>]

Print Reply
Jyh-Ming Lien <[log in to unmask]>
Reply To:
Jyh-Ming Lien <[log in to unmask]>
Thu, 28 Feb 2013 14:55:48 -0500
text/plain (82 lines)
[Apologies for multiple postings]

Please note that this talk starts at 12:30pm.

Professor Lin currently has two GRA openings.
PhD students looking for advisors are particularly
encouraged to attend.

*    GRAND Seminar
*    http://cs.gmu.edu/~robotics/pmwiki.php/Main/GrandSeminar


Discovery of Novel Patterns in Massive Time Series Data


March 5, 12:30pm, Tuesday, 2013
ENGR 4201


Jessica Lin
Associate Professor
Department of Computer Science
George Mason University


Massive amounts of data are generated daily at a rapid rate. As a
result, the world is faced with unprecedented challenges and
opportunities on managing the ever-growing data, and much of the
world's supply of data is in the form of time series. One obvious
problem of handling time series databases concerns with its typically
massive sizeógigabytes or even terabytes are common, with more and
more databases reaching the petabyte scale. Most classic data mining
algorithms do not perform or scale well on time series data due to
their unique structure. In particular, the high dimensionality, very
high feature correlation, and the typically large amount of noise that
characterize time series data present a difficult challenge. As a
result, time series data mining has attracted an enormous amount of
attention in the past two decades. This presentation gives an overview
of my contributions in the field of time series data mining. The first
part of the presentation discusses time series data mining
fundamentals - more specifically, the two aspects that hugely
determine the efficiency and effectiveness of most time series data
mining algorithms: data representation and similarity measure. The
second part of the presentation will focus on the discovery of novel
and non-trivial patterns in time series data, including frequently
encountered (or repeated) patterns, rare (or anomalous) patterns, or
latent structure.


Dr. Jessica Lin is an Associate Professor in the Department of
Computer Science at George Mason University. She received her PhD
degree from University of California, Riverside in June, 2005. Her
research interests encompass broad areas of data mining, especially
data mining for large temporal and spatiotemporal databases, text, and
images. Over the years, she has collaborated with researchers from
various domains including medicine, earth sciences, manufacturing,
national defense, and astronomy. Her research is partially funded by
NSF, US Army and Intel Corporation.

Jyh-Ming Lien
Assistant Professor, George Mason University

MASC Group: http://masc.cs.gmu.edu
Homepage: http://cs.gmu.edu/~jmlien