Shape Identification in Temporal Data Sets

TitleShape Identification in Temporal Data Sets
Publication TypeTheses
Year of Publication2009
AuthorsGregory MB, Shneiderman B
Date Published2009///
UniversityMaster’s thesis, University of Maryland

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 are abstract. Details
such as the height of spike or its rate increase, are lost in the ab-
straction. These hidden details make it difficult to define shapes
and compare one to another. For example, what attributes of a
spike determine its “spikiness”? The ability to define and com-
pare shapes is important because it allows shapes to be identified
and ranked, according to an attribute of interest. Work has been
done in the area of shape identification through pattern matching
and other data mining techniques, but ideas combining the identifi-
cation and comparison of shapes have received less attention. This
paper fills the gap by presenting a set of shapes and the attributes
by which they can identified, compared, and ranked. Neither the set
of shapes, nor their attributes presented in this paper are exhaustive,
but it provides an example of how a shape’s attributes can be used
for identification and comparison. The intention of this paper is not
to replace any particular mathematical method of identifying a par-
ticular behavior, but to provide a toolset for knowledge discovery
and an intuitive method of data mining for novices. Spikes, sinks,
rises, drops, lines, plateaus, valleys, and gaps are the shapes pre-
sented in this paper. Several attributes for each shape are defined.
These attributes will be the basis for constructing definitions that
allow the shapes to be identified and ranked. The second contri-
bution is an information visualization tool, TimeSearcher: Shape
Search Edition (SSE), which allows users to explore data sets using
the identification and ranking ideas in this paper.