Automatic pattern tracking and visualization for decision support

Monitoring autonomous systems plays a key role in understanding the systems’ functionality and in ensuring their safety. A fundamental challenge thereby is the size, complexity and diversity of the data that is continuously generated and has been taken into account to make fast assessments and informed decision.

Without appropriate data reduction and visual support this is hardly possible.

This project is to establish methods for selection, tracking and visualization of patterns in multifield data to support decision making based on monitoring data from autonomous systems.

The project will consist of two parts, the first part lays the theoretical foundations for multifield pattern tracking, the second part focuses on the pattern selection, visualization and interaction with the tracking results.

Pattern extraction and tracking

Pattern of interest can exhibit complex shapes and structures and can be based on multiple data sources. Descriptors are explored for extraction and tracking of patterns with variations, including geometric distortion, rotation, translation, scale and variations relate to background noise and partially missing data.

Visualization for pattern selection and analysis

Multiple linked visualizations of the individual fields will be provided for interactive selection and visual analysis of patterns of interest. When appropriate we will also provide a pattern editor to support pattern sketching. We will support online changes and refinement of pattern definitions. The goal of the visualization is to support the analysis of selected patterns, follow them over time and observe changes in size and expression while also providing the data context.


WASP research at MIT