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Situational Awareness Interface (SAI), 2022

A project in collaboration with the NCSU Laboratory for Analytic Sciences exploring the question, how might the design of an interface use the affordances of Machine Learning (ML) to provide a personalized user experience so that the analyst might quickly and knowledgeably enter the day’s workflow?

User Interface, User Experience, and Machine Learning/AI

This was a class project at NCSU completed with graphic design students Amanda Williams and Riley Walman.

*Please note that the data and details depicted in these personas and use cases are based on unclassified, fictitious scenarios.

User Scenario

Nyah, a Senior Search and Discovery Data Analyst called in to surge on a crisis situation.

As Surge Team member, Nyah has received instructions to investigate a crisis in Kronos where GASTech employees have been kidnapped. The early stages of a surge are often very chaotic, so Nyah needs to efficiently gain contextual awareness of the event in order to assess the situation and discover the location of the missing employees and how to get them home.

Identified Pain Points

  • Difficult to get a case overview with the amount of initial available information in multiple windows or tabs

  • Strenuous to immediately identify anomalous activity in the case

  • Copious amounts and iterations of queries need to run in short period of time 

Established user journey for Nyah’s day from a birds-eye view. Major things to note are the amount of red at the bottom, indicating a pain point that isn’t resolved in her current day.

Scenario video for the Situational Awareness Interface.

Color Accessibility

Interface colors are accessible for analysts with color-related visual impairments.

AI-Prioritized Key

Priority is established by ML based on what the analyst needs to know to efficiently gain contextual awareness.

Textual Summaries

AI generates summaries from all case data to provide an overview of the situation. It also sifts through customer interest analyses to incorporate customer interest trends. The supervisor summary provides the human perspective from the analyst’s supervisor.

Interface Features

AI-Prioritized Content

AI customizes tree map display of patterns based on key prioritization and also uses scale as a secondary identifier of priority.

Interactive Timeline

AI highlights high priority events important for the analyst to gain situational awareness based on their preferences/feedback. ‘Predicted behaviors and events’ are available to analysts if they scroll past the present and into the future. Each square is an event relating to the crisis. Analysts can scroll though the timeline to see major events in the case.

The Design Process

Mapping the Datasets

In order to understand the type of content our user encounters in her job, we broke down her workflow and mapped out the data involved in the user scenario. 

Sketches + Early Iterations

Sketching and exploring potential concepts for features on the analyst dashboard.


Wider Implications
  • Summaries provide top-level overview while other features equip analyst to drill down into specific data points

  • Enables the analyst to intuitively understand the prioritization system via color and hierarchy 

  • Using ML provides rich opportunities for identifying and responding to anomalous behavior in the case

Future Considerations
  • How could SAI incorporate cross-collaboration between analysts?

  • In addition to using the watch’s proximity sensing to enhance data security (monitor locking), how else might the watch be leveraged to support security and awareness?

  • In addition to the 24-hour updating cycle, how might SAI streamline the reporting process?

Customized Display

Analyst can adjust AI-selected features using the menu.

Points of Interest

Feature shows high priority locations in the case and AI-predicted routes between locations.

Key Players: Image vs. Text View

The key players wheel Image allows analysts to quickly see key players involved in a case and their affiliations. ‘Text View’ offers an alternative view for analysts who prefer textually-presented data.

Textual view of key player tracker in context.

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