An Extensible Framework for Provenance in Human Terrain Visual Analytics

Abstract

Fig. 1: Graphical summaries of bookmarks are used to record and browse the analytical process, here ordered (row-by-row) in the sequence in which they were bookmarked. Each can be used to access the live data, enabling analysts to revisit parts of the analytical process and helping verify past interpretations. A legend describing the encodings is provided in Fig. 6.Abstract-We describe and demonstrate an extensible framework that supports data exploration and provenance in the context of Human Terrain Analysis (HTA). Working closely with defence analysts we extract requirements and a list of features that characterise data analysed at the end of the HTA chain. From these, we select an appropriate non-classified data source with analogous features, and model it as a set of facets. We develop ProveML, an XML-based extension of the Open Provenance Model, using these facets and augment it with the structures necessary to record the provenance of data, analytical process and interpretations. Through an iterative process, we develop and refine a prototype system for Human Terrain Visual Analytics (HTVA), and demonstrate means of storing, browsing and recalling analytical provenance and process through analytic bookmarks in ProveML. We show how these bookmarks can be combined to form narratives that link back to the live data. Throughout the process, we demonstrate that through structured workshops, rapid prototyping and structured communication with intelligence analysts we are able to establish requirements, and design schema, techniques and tools that meet the requirements of the intelligence community. We use the needs and reactions of defence analysts in defining and steering the methods to validate the framework.

SensePath: Understanding the Sensemaking Process Through Analytic Provenance

“…However, this will not work if the method introduces considerable distraction or does not offer any benefits. Allowing user annotation is one of the most common forms [27,38]: the user creates notes or annotations that record comments, findings, or hypotheses. Those notes can be associated with the visualization, allowing users returning to the states when the notes were made [30,35] to re-examine the context or investigate further.…”

“…For example, users are more likely to record the findings they made than the process or approach that led them there. To encourage user to write richer notes, a visual analytic system needs to provide additional benefits such as the ability to create visual narratives [38] that reveals the reasoning process and help users review and plan exploratory analysis for complex sensemaking task after recording the current progress [26].…”

“…Existing approaches can be broadly categorized into manual and automatic capture methods. The manual methods [27,38] largely rely on users to record their analysis process through note taking, whereas the automatic methods so far can identify a group of actions that are likely to be part of the same sub-task without knowing what the sub-task actually is [17]. There is limited success of automated inference of sub-tasks and tasks from lower level events and actions [42].…”

SenseMap: Supporting browser-based online sensemaking through analytic provenance

“…A narrative can include provenance information at different levels: an analysis result, user notes, visualizations and raw data. DIVA [37] allows users to create a narrative based on user annotations and captured visualization states, and makes it possible to revisit the visualizations as when they were captured. SchemaLine [21] enables narrative construction by grouping user notes along the timeline.…”

Visual analytics of GPS tracks: From location to place to behaviour

“…We followed a faceted design model [8] for creating different views of the data, each emphasising different fundamental types.…”