ICE: An Interactive Configuration Explorer for High Dimensional
Categorical Parameter Spaces
release_kasfp2pd6fd6tibcqvxhvxqaia
by
Anjul Tyagi, Zhen Cao, Tyler Estro, Erez Zadok, Klaus Mueller
2020
Abstract
There are many applications where users seek to explore the impact of the
settings of several categorical variables with respect to one dependent
numerical variable. For example, a computer systems analyst might want to study
how the type of file system or storage device affects system performance. A
usual choice is the method of Parallel Sets designed to visualize multivariate
categorical variables. However, we found that the magnitude of the parameter
impacts on the numerical variable cannot be easily observed here. We also
attempted a dimension reduction approach based on Multiple Correspondence
Analysis but found that the SVD-generated 2D layout resulted in a loss of
information. We hence propose a novel approach, the Interactive Configuration
Explorer (ICE), which directly addresses the need of analysts to learn how the
dependent numerical variable is affected by the parameter settings given
multiple optimization objectives. No information is lost as ICE shows the
complete distribution and statistics of the dependent variable in context with
each categorical variable. Analysts can interactively filter the variables to
optimize for certain goals such as achieving a system with maximum performance,
low variance, etc. Our system was developed in tight collaboration with a group
of systems performance researchers and its final effectiveness was evaluated
with expert interviews, a comparative user study, and two case studies.
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