Phong H. Nguyen

A User Study on Curved Edges in Graph Visualization

Recently there has been increasing research interest in displaying graphs with curved edges to produce more readable visualizations. While there are several automatic techniques, little has been done to evaluate their effectiveness empirically. In this paper we present two experiments studying the impact of edge curvature on graph readability. The goal is to understand the advantages and disadvantages of using curved edges for common graph tasks compared to straight line segments, which are the conventional choice for showing edges in node-link diagrams. We included several edge variations: straight edges, edges with different curvature levels, and mixed straight and curved edges. During the experiments, participants were asked to complete network tasks including determination of connectivity, shortest path, node degree, and common neighbors. We also asked the participants to provide subjective ratings of the aesthetics of different edge types. The results show significant performance differences between the straight and curved edges and clear distinctions between variations of curved edges.

An Extensible Framework for Provenance in Human Terrain Visual Analytics

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.

SchemaLine: Timeline Visualization for Sensemaking

Timeline visualization is an important tool for sense making. It allows analysts to examine information in chronological order and to identify temporal patterns and relationships. However, many existing timeline visualization methods are not designed for the dynamic and iterative nature of the sense making process and the various analysis activities it involves. In this paper, we introduce a novel timeline visualization, Schema Line, to address these deficiencies. Schema Line is designed to group notes into analyst-determined schema, using a layout algorithm to produce compact but aesthetically pleasing timeline visualization, and includes fluid user interactions to support sense making activities. It enables interactive temporal schemata construction with seamless integration with visual data exploration and note taking. Our preliminary evaluation results show that the participants found the new method easy to learn and use, and its features effective for the sense making activities for which it was designed.

SensePath: Understanding the Sensemaking Process Through Analytic Provenance

Sensemaking is described as the process of comprehension, finding meaning and gaining insight from information, producing new knowledge and informing further action. Understanding the sensemaking process allows building effective visual analytics tools to make sense of large and complex datasets. Currently, it is often a manual and time-consuming undertaking to comprehend this: researchers collect observation data, transcribe screen capture videos and think-aloud recordings, identify recurring patterns, and eventually abstract the sensemaking process into a general model. In this paper, we propose a general approach to facilitate such a qualitative analysis process, and introduce a prototype, SensePath, to demonstrate the application of this approach with a focus on browser-based online sensemaking. The approach is based on a study of a number of qualitative research sessions including observations of users performing sensemaking tasks and post hoc analyses to uncover their sensemaking processes. Based on the study results and a follow-up participatory design session with HCI researchers, we decided to focus on the transcription and coding stages of thematic analysis. SensePath automatically captures users sensemaking actions, i.e., analytic provenance, and provides multi-linked views to support their further analysis. A number of other requirements elicited from the design session are also implemented in SensePath, such as easy integration with existing qualitative analysis workflow and non-intrusive for participants. The tool was used by an experienced HCI researcher to analyze two sensemaking sessions. The researcher found the tool intuitive and considerably reduced analysis time, allowing better understanding of the sensemaking process.

TimeSets: timeline visualization with set relations

In this article, we introduce a novel timeline visualization technique, TimeSets, that helps make sense of complex temporal datasets by showing the set relationships among individual events. TimeSets visually groups events that share a topic, such as a place or a person, while preserving their temporal order. It dynamically adjusts the level of detail for each event to suit the amount of information and display estate. Various design options were explored to address issues such as one event belonging to multiple topics. A controlled experiment was conducted to evaluate its effectiveness by comparing it to the KelpFusion method. The results showed significant advantage in accuracy and user preference.

SenseMap: Supporting browser-based online sensemaking through analytic provenance

Sensemaking is described as the process in which people collect, organize and create representations of information, all centered around some problem they need to understand. People often get lost when solving complicated tasks using big datasets over long periods of exploration and analysis. They may forget what they have done, are unaware of where they are in the context of the overall task, and are unsure where to continue. In this paper, we introduce a tool, SenseMap, to address these issues in the context of browser-based online sensemaking. We conducted a semi-structured interview with nine participants to explore their behaviors in online sensemaking with existing browser functionality. A simplified sensemaking model based on Pirolli and Cards model is derived to better represent the behaviors we found: users iteratively collect information sources relevant to the task, curate them in a way that makes sense, and finally communicate their findings to others. SenseMap automatically captures provenance of user sensemaking actions and provides multi-linked views to visualize the collected information and enable users to curate and communicate their findings. To explore how SenseMap is used, we conducted a user study in a naturalistic work setting with five participants completing the same sensemaking task related to their daily work activities. All participants found the visual representation and interaction of the tool intuitive to use. Three of them engaged with the tool and produced successful outcomes. It helped them to organize information sources, to quickly find and navigate to the sources they wanted, and to effectively communicate their findings.

Visual analysis of streaming data with SAVI and SenseMAP

Two tools were developed for the analysis tasks in the VAST Challenge 2014 Mini-Challenge 3: Social Analytics VIsualiszation (SAVI) and Sense Making with Analytic Provenance (SenseMAP).

POLAR-An Interactive Patterns of Life Visualisation Tool for Intelligence Analysis

POLAR is an experimental test-bed visualisation tool for Patterns of Life analysis, developed on the basis of knowledge elicitation with stakeholders. It uses multiple and coordinated views for exploring geo-temporal datasets. The system has three modes of interaction for addressing different kinds of PoL questions. It supports the exploration of movement patterns with resolutions ranging from intercontinental to local travel and a year or more to just a few minutes.

Concern level assessment: building domain knowledge into a visual system to support network-security situation awareness

Information officers and network administrators require tools to help them achieve situation awareness about potential network threats. We describe a response to mini-challenge 1 of the 2012 IEEE Visual Analytics Science and Technology challenge in which we developed a visual analytic solution to a network-security situation awareness problem. To support conceptual design, we conducted a series of knowledge elicitation sessions with domain experts. These provided an understanding of the information they needed to make situation awareness judgements as well as a characterisation of those judgements in the form of production rules, which define a parameter we called the ‘concern level assessment’. The concern level assessment was used to provide heuristic guidance within a visual analytic system called Middlesex Spatial Interactive Visualisation Environment. An analysis of Visual Analytics Science and Technology challenge assessment sessions using Middlesex Spatial Interactive Visualisation Environment provides some evidence that intelligent heuristics like these can provide useful guidance without unduly dominating interaction and understanding.

M-Sieve: A visualisation tool for supporting network security analysts: VAST 2012 Mini Challenge 1 award: “Subject matter expert's award”

The Middlesex Spatial Interactive Visualisation Environment (M-Sieve) is a spatiotemporal visual analytics tool for exploring computer network activity. M-Sieve allows the user to filter and visualize data through facets to explore and find patterns. To help guide exploration, we developed a set of rules which are used to derive a variable we call the ‘Concern Level Assessment’ (CLA). The CLA is based on attributes of nodes on the network. The rules were developed by eliciting inferences from network security domain experts. The combination of M-Sieve and the CLA allowed us to address the problem presented by the VAST 2012 Competition — Mini Challenge 1.