Abstract
We investigate the design of ontology-supported, progressively disclosed visual analytics interfaces for searching and triaging large document sets. The goal is to distill a set of criteria that can help guide the design of such systems. We begin with a background of information search, triage, machine learning, and ontologies. We review research on the multi-stage information-seeking process to distill the criteria. To demonstrate their utility, we apply the criteria to the design of a prototype visual analytics interface: VisualQUEST (Visual interface for QUEry, Search, and Triage). VisualQUEST allows users to plug-and-play document sets and expert-defined ontology files within a domain-independent environment for multi-stage information search and triage tasks. We describe VisualQUEST through a functional workflow and culminate with a discussion of ongoing formative evaluations, limitations, future work, and summary.
Highlights
Visual analytics combines the strengths of machine learning (ML) techniques, visualizations, and interaction to help users explore data/information and achieve their analytic tasks [1]
We had formative user evaluations of VisualQUEST—that is, ongoing, task-driven assessments of the effectiveness of the search and triage interface. These evaluations have been informal, involving volunteers associated with our research lab. The feedback from these users helped us gain some insights into how the design of ontology-supported and progressively disclosed Visual analytics tools (VATs) interfaces can be guided by the collection of design criteria that we discussed in this paper
We investigated the design of ontology-supported, progressively disclosed visual analytics interfaces for searching and triaging large document sets
Summary
Visual analytics combines the strengths of machine learning (ML) techniques, visualizations, and interaction to help users explore data/information and achieve their analytic tasks [1] This joint human–computer coupling is more complicated than an internal automated analysis augmented with an external visualization of results seen by users. Visual analytics tools (VATs) help users form valuable connections with their information and be more active participants in the analysis process [4,5] They can be used to support a wide variety of domain tasks, such as making sense of misinformation, searching large document sets, and making decisions regarding health data, to name a few [6,7,8,9]. This model was refined by Vakkari into a three-part model of pre-focus (initiation, selection, exploration), focus formulation (formulation), and post-formulation (collection and presentation) [15]
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