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THE THESIS DEFENSE FOR THE M.S. DEGREE IN COMPUTER SCIENCE FOR

				Machon Gregory

Will be held:
DATE:			Friday December 5, 2008 at 3:00 p.m.
LOCATION:		Room 3258 A.V. Williams Building

TITLE: 			Shape Identification and Ranking in Temporal Data
Sets
		
ABSTRACT:	Shapes are a concise way to describe temporal variable
behaviors. Some commonly used shapes are spikes, sinks, rises, and drops. A
spike describes a set of variable values that rapidly increase, then
immediately rapidly decrease. The variable may be the value of a stock or a
person's blood sugar levels. Shapes abstractly describe a variable's
behavior. Details such as the height of spike or its rate increase, are lost
in the abstraction. These hidden details make it difficult to define shapes
and compare one instance to another. For example, what attributes can be
used to define a spike's behavior? And what attributes of a spike determine
its "spikiness"? The ability to define and compare shapes is important
because it allows shapes to be identified and ranked, according to an
attribute of interest. A lot of work has been done 
in the area of shape identification through pattern matching and other data
mining techniques, but ideas combining the identification and comparison of
shapes have received less attention. 
 
This dissertation fills the gap by presenting a set of shapes and their
attributes, by which they can identified, compared, and ranked. Neither the
set of shapes, nor their attributes presented in this dissertation are
exhaustive, but it provides an example of how a shape's attributes can be
used for identification and comparison. Spikes, sinks, rises, drops, lines,
plateaus, valleys, and gaps are the shapes presented in this dissertation.
Several attributes for each shape are identified and defined. These
attributes will be the basis for constructing definitions that identify a
particular behavior of a shape and allow it to be ranked. 
 
The second contribution of this work is an information visualization tool,
TimeSearcher: Shape Search Edition (SSE), which allows users to explore data
sets using the identification and ranking ideas, presented in this
dissertation. Case studies were performed to evaluate the benefit of shape
identification and ranking in different data sets. Four case studies were
performed with a single user, exploring network traffic data and X-ray
diffraction data. 

Examining Committee:

COMMITTEE CHAIR:		Dr. Ben Shneiderman
Committee Member(s):		Dr. Ben Bederson
				Dr. Dana Nau
				Dr. Catherine Plaisant	
			
EVERYONE IS INVITED TO ATTEND THE PRESENTATION PORTION OF THIS DEFENSE