Situational Awareness Interface (SAI)
Laboratory for Analytical Sciences - Machine Learning Interface for Data Analysts
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?
This was a class project at NCSU that I 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 Journey Map
Nyah, a Senior Search + Discovery Analyst
As Surge Team member, Nyah has received instructions to investigate a crisis in the fictional country of 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
Scenario Video: Situational Awareness Interface
AI customizes tree map display of patterns based on key prioritization and also uses scale as a secondary identifier of priority.
Priority is established by ML based on what the analyst needs to know to efficiently gain contextual awareness.
Interface colors are accessible for analysts with visual impairments.
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.
Analyst can adjust AI-selected features using the menu.
Each square is an event relating to the crisis. Analysts can scroll though the timeline to see major events in the case.
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.
Points of Interest
Feature shows high priority locations in the case and AI-predicted routes between locations.
Image vs. Text View
Offers an alternative view for analysts who prefer textually-presented data.
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
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?