Mixed-Initiative Data Visualization Graph Layout
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Solution Overview
Problem
Current techniques for sense making operations on data sets are inefficient due to the need for manual viewing of both in-line and annotation summary views, and configuring text mining tools to detect salient aspects is time-consuming and requires expertise.
Innovation Solution
A computer-implemented method that computes pairwise similarities among nodes in a data set, generates a graph layout based on user-specified constraints, and renders a graph for display, enabling a mixed-initiative visualization approach to facilitate efficient analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual viewing of both in-line and annotation summary views is used, then users can analyze annotations in context, but the process is time-consuming and inefficient
Solution Approach 1:
The patent combines the in-line view and annotation summary view into a single integrated graph visualization. Nodes representing data items and annotations are positioned spatially to show both contextual relationships (via edges) and annotation patterns (via clustering), eliminating the need to manually switch between separate views while maintaining analysis accuracy.
2Productivity
If text mining tools are configured to automatically detect patterns, then analysis time is reduced, but configuration requires expertise and time
Solution Approach 1:
The system automatically generates the graph layout and computes pairwise similarities without requiring user configuration of text mining parameters. The annotation graph is constructed self-service style by computing similarities among nodes and automatically positioning them, eliminating the need for expert configuration while maintaining high analysis efficiency.
3Productivity
If automated graph generation is used, then visualization is efficient, but user control over layout is limited
Solution Approach 1:
The graph layout is dynamically adjustable based on user-specified constraints. Users can specify constraints such as fixing certain nodes at particular positions or requiring specific spatial relationships, and the system recomputes the layout to satisfy these constraints while maintaining the overall automated generation process. This allows both efficient visualization and user control flexibility.
Data Source
AI summary
In various embodiments, a visualization engine generates graphs that facilitate sense making operations on data sets. A graph includes nodes that are associated with a data set and edges that represent relationships between the nodes. In operation, the visualization engine computes pairwise similarities between the nodes. Subsequently, the visualization engine computes a layout for the graph based on the pairwise similarities and user-specified constraints. Finally, the visualization engine renders a graph for display based on the layout, the nodes, and the edges. Advantageously, by interactively specifying constraints and then inspecting the topology of the automatically generated graph, the user may efficiently explore salient aspects of the data set.