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While there have been intensive efforts in developing better 3D flow visualization techniques, little attention has
been paid to the design of better user interfaces and more effective data exploration work flow. In this paper, we
propose a novel graph-based user interface called Flow Web to enable more systematic explorations of 3D flow
data.
The Flow Web is a node-link graph that is constructed to highlight the essential flow structures where a
node represents a region in the field and a link connects two nodes if there exist particles traveling between
the regions. The direction of an edge implies the flow path, and the weight of an edge indicates the number of
particles traveling through the connected nodes. Hierarchical flow webs are created by splitting or merging nodes
and edges to allow for easy understanding of the underlying flow structures. To draw the Flow Web, we adopt
force based graph drawing algorithms to minimize edge crossings, and use a hierarchical layout to facilitate the
study of flow patterns step by step. The Flow Web also supports user queries to the properties of nodes and
links. Examples of the queries for node properties include the degrees, complexity, and some associated physical
attributes such as velocity magnitude. Queries for edges include weights, flow path lengths, existence of circles
and so on. It is also possible to combine multiple queries using operators such as and , or, not. The FlowWeb
supports several types of user interactions. For instance, the user can select nodes from the subgraph returned
by a query and inspect the nodes with more details at different levels of detail.
There are multiple advantages of using the graph-based user interface. One is that the user can identify
regions of interest much more easily since, unlike inspecting 3D regions, there is very little occlusion. It is also
much more convenient for the user to query statistical information about the nodes and links at different levels of
detail. With the Flow Web, it becomes easier for the user to log and track the progress of data exploration which
is crucial for exploring large data sets. We demonstrate how to construct and draw the Flow Web effectively,
and how to query the Flow Web to retrieve useful information from the data. Case studies are provided to
demonstrate the exploration process.
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