  The use of large-scale multifaceted data is common in a wide variety of
scientific applications. In many cases, this multifaceted data takes the form
of a field-based (Eulerian) and point/trajectory-based (Lagrangian)
representation as each has a unique set of advantages in characterizing a
system of study. Furthermore, studying the increasing scale and complexity of
these multifaceted datasets is limited by perceptual ability and available
computational resources, necessitating sophisticated data reduction and feature
extraction techniques. In this work, we present a new 4D feature
segmentation/extraction scheme that can operate on both the field and
point/trajectory data types simultaneously. The resulting features are
time-varying data subsets that have both a field and point-based component, and
were extracted based on underlying patterns from both data types. This enables
researchers to better explore both the spatial and temporal interplay between
the two data representations and study underlying phenomena from new
perspectives. We parallelize our approach using GPU acceleration and apply it
to real world multifaceted datasets to illustrate the types of features that
can be extracted and explored.
