This technology offers advanced methods in data graphing and visualization, providing a novel multi-level, middle-out cross-zooming approach. It addresses the immediate market need for efficient data analysis and visualization tools, particularly within industries handling large and complex data sets.

Industries handling vast and complex data sets often struggle with effective data visualization and analysis, leading to inefficiencies and oversight. This impacts decision-making processes and operational efficiency. Pacific Northwest National Laboratory (PNNL) has developed a technology to overcome these barriers.

At the core, this technology utilizes a multi-level hierarchical approach to graph analytics, enabling users to explore data at varying resolutions. The middle-out, cross-zooming feature allows analysts to start at an intermediate level and navigate across different hierarchical levels seamlessly. This method significantly enhances interpretability and efficiency in data analysis, reducing the time and effort required to extract meaningful insights from large data sets.

PNNL scientists have engineered this technology to provide dynamic and interactive visualizations that adjust to user inputs, allowing for simultaneous coarse and fine detail access. This innovation not only addresses the visualization challenges but also provides a robust platform for sophisticated data interrogation and analysis, improving industry productivity.

APPLICABILITY
This technology is ideal for use in industries, such as data science, cybersecurity, and bioinformatics, where large graph datasets are prevalent. Potential users include data analysts, industry professionals, and organizations seeking enhanced data visualization capabilities.

DESIGN

Here, Green Hornet is visualizing the shortest path between two nodes (highlighted in yellow) within a fictional paper with a co-authored data set.