A Vocabulary Approach to Partial Streamline Matching and Exploratory Flow Visualization

FlowNL: Asking the Flow Data in Natural Languages

Flow visualization is essentially a tool to answer domain experts' questions about flow fields using rendered images. Static flow visualization approaches require domain experts to raise their questions to visualization experts, who develop specific techniques to extract and visualize the flow structures of interest. Interactive visualization approaches allow domain experts to ask the system directly through the visual analytic interface, which provides flexibility to support various tasks. However, in practice, the visual analytic interface may require extra learning effort, which often discourages domain experts and limits its usage in real-world scenarios. In this paper, we propose FlowNL, a novel interactive system with a natural language interface. FlowNL allows users to manipulate the flow visualization system using plain English, which greatly reduces the learning effort. We develop a natural language parser to interpret user intention and translate textual input into a declarative language. We design the declarative language as an intermediate layer between the natural language and the programming language specifically for flow visualization. The declarative language provides selection and composition rules to derive relatively complicated flow structures from primitive objects that encode various kinds of information about scalar fields, flow patterns, regions of interest, connectivities, etc. We demonstrate the effectiveness of FlowNL using multiple usage scenarios and an empirical evaluation.

Integral Curve Clustering and Simplification for Flow Visualization: A Comparative Evaluation

Unsupervised clustering techniques have been widely applied to flow simulation data to alleviate clutter and occlusion in the resulting visualization. However, there is an absence of systematic guidelines for users to evaluate (both quantitatively and visually) the appropriate clustering technique and similarity measures for streamline and pathline curves. In this work, we provide an overview of a number of prevailing curve clustering techniques. We then perform a comprehensive experimental study to qualitatively and quantitatively compare these clustering techniques coupled with popular similarity measures used in the flow visualization literature. Based on our experimental results, we derive empirical guidelines for selecting the appropriate clustering technique and similarity measure given the requirements of the visualization task. We believe our work will inform the task of generating meaningful reduced representations for large-scale flow data and inspire the continuous investigation of a more refined guidance on clustering technique selection.

Semantic Flow Graph: A Framework for Discovering Object Relationships in Flow Fields

Visual exploration of flow fields is important for studying dynamic systems. We introduce semantic flow graph (SFG), a novel graph representation and interaction framework that enables users to explore the relationships among key objects (i.e., field lines, features, and spatiotemporal regions) of both steady and unsteady flow fields. The objects and their relationships are organized as a heterogeneous graph. We assign each object a set of attributes, based on which a semantic abstraction of the heterogeneous graph is generated. This semantic abstraction is SFG. We design a suite of operations to explore the underlying flow fields based on this graph representation and abstraction mechanism. Users can flexibly reconfigure SFG to examine the relationships among groups of objects at different abstraction levels. Three linked views are developed to display SFG, its node split criteria and history, and the objects in the spatial volume. For simplicity, we introduce SFG construction and exploration for steady flow fields with critical points being the only features. Then we demonstrate that SFG can be naturally extended to deal with unsteady flow fields and multiple types of features. We experiment with multiple data sets and conduct an expert evaluation to demonstrate the effectiveness of our approach.

Blockwise Human Brain Network Visual Comparison Using NodeTrix Representation

Visually comparing human brain networks from multiple population groups serves as an important task in the field of brain connectomics. The commonly used brain network representation, consisting of nodes and edges, may not be able to reveal the most compelling network differences when the reconstructed networks are dense and homogeneous. In this paper, we leveraged the block information on the Region Of Interest (ROI) based brain networks and studied the problem of blockwise brain network visual comparison. An integrated visual analytics framework was proposed. In the first stage, a two-level ROI block hierarchy was detected by optimizing the anatomical structure and the predictive comparison performance simultaneously. In the second stage, the NodeTrix representation was adopted and customized to visualize the brain network with block information. We conducted controlled user experiments and case studies to evaluate our proposed solution. Results indicated that our visual analytics method outperformed the commonly used node-link graph and adjacency matrix design in the blockwise network comparison tasks. We have shown compelling findings from two real-world brain network data sets, which are consistent with the prior connectomics studies.