| Abstract | Large scale scientific simulations are increasingly generatingvery large data sets that present substantial challenges to
current visualization systems. In this paper, we develop a new
scalable and efficient scheme for the visual exploration of 4-D
isosurfaces of time varying data by rendering the 3-D isosurfaces
obtained through an arbitrary axis-parallel hyperplane cut. The
new scheme is based on: (i) a new 4-D hierarchical indexing
structure, called Information-Aware Octree; (ii) a
controllable delayed fetching technique; and (iii) an optimized
data layout. Together, these techniques enable efficient and
scalable out-of-core visualization of large scale time varying
data sets. We introduce an entropy-based dimension integration
technique by which the relative resolutions of the spatial and
temporal dimensions are established, and use this information to
design a compact size 4-D hierarchical indexing structure. We also
present scalable and efficient techniques for out-of-core
rendering. Compared with previous algorithms for constructing 4-D
isosurfaces, our scheme is substantially faster and requires much
less memory. Compared to the Temporal Branch-On-Need octree
(T-BON), which can only handle a subset of our queries, our
indexing structure is an order of magnitude smaller and is at
least as effective in dealing with the queries that the T-BON can
handle. We have tested our scheme on two large time-varying data
sets and obtained very good performance for a wide range of
isosurface extraction queries using an order of magnitude smaller
indexing structures than previous techniques. In particular, we
can generate isosurfaces at intermediate time steps very quickly.
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