As the speed of computers continues to increase at a very fast rate,
the size of data generated from scientific simulations has now reached
petabytes ($10^{12}$ bytes) and beyond. Under such circumstances, no
existing techniques can be used to perform effective data analysis at
a full precision. To analyze large scale data sets, visual analytics
techniques with effective summarization and flexible interface are
crucial in assisting the exploration of data at different levels of
detail. To improve data access efficiency, summarization and triage are important
components for categorizing data items according to their saliency. This will
allow the user to focus only on the relevant portion of data.
In this dissertation, several visualization and analysis techniques
are presented to facilitate the analysis of multivariate time-varying data and
flow fields. For multivariate time-varying data sets, data items are
categorized based on the values over time to provide an effective
overview of the time-varying phenomena. From the similarity to the
user-specified feature, dynamic phenomena across multiple variables in
different spatial and temporal domains can be explored.
To visualize flow fields, information theory is used to model the
local flow complexity quantitatively. Based on the model, an
information-aware visualization framework is designed to create images
with different levels of visual focus according to the local flow
complexity. By extending the measurement from object space to image
space, visualization primitives can be further rearranged, leading to
more effective visualization of salient flow features with less
occlusion.