MAVERICK: A Synthetic Murder Mystery Network Dataset to Support Sensemaking Research

AbstractThe MAVERICK dataset was created to support a series of empirical studies looking at the effectiveness of network visualizations intended to support information foraging and human sensemaking within the domain of counterinsurgency intelligence analysis. This synthetic dataset is structured as a forensic mystery with the central goal of solving a fictional murder. The dataset includes 181 text-based reports, with additional media included with some messages as attachments, collected from various sources of varying reliability. The reports are framed as being collected from the perspective of a reporter investigating the murder through interviews with suspects and observations taken at the site the murder. The dataset includes intentional and unintentional deception along with calculated source reliabilities based on available evidence. The dataset is dynamic in nature, as the information in the dataset evolves and expands over a simulated period of time. This is done to both to simulate a real-world scenario, and to allow for evolutionary tasks and experiments to be performed using the dataset. The dataset is designed to be complex enough to simulate a real-world, while remaining accessible to individuals without experience in a specific domain of interest. This meant that it had to be on a topic that did not require prior domain knowledge to understand available information or to understand what strategies should be applied during analysis of the dataset. The solution to these challenges was the development of a fictional murder mystery. The plot involves a murder that took place over the course of a weekend with several possible suspects at a large private estate. This scenario allowed for a great deal of complexity; however, it was also a subject matter that could be easily understood by participants without prerequisite domain experience

Technology and Information Fusion Needs to Address the Food, Energy, Water Systems (FEWS) Nexus Challenges

In response to the Food, Energy, Water Systems (FEWS) Nexus Challenge grant awarded by NSF, the team of investigators led by David Ebert, along with Christian Butzke, Melba Crawford, Phillip Owens, and Dimitrios Peroulis conducted a two-day workshop in Napa, California on November 5th and 6th, 2015. The workshop addressed the emerging issues in the food/energy/water systems throughout the diverse geography of the United States and over various crops and environmental conditions to better understand and model and ultimately devise a solution for the challenges to the FEWS nexus. One of the intended outcomes of the workshop was to generate a report that will chart the research challenges and opportunities for solving these challenges and have an impact on scientific fields including, sensing technology, hydrology, soil science, climate, data fusion, analysis, visualization, and data driven decision 2 making, as well as agricultural production, local and regional economies, sustainability and planning. The information contained in this post-workshop report serves as that foundation.In response to the Food, Energy, Water Systems (FEWS) Nexus Challenge grant awarded by NSF, the team of investigators led by David Ebert, along with Christian Butzke, Melba Crawford, Phillip Owens, and Dimitrios Peroulis conducted a two-day workshop in Napa, California on November 5th and 6th, 2015. The workshop addressed the emerging issues in the food/energy/water systems throughout the diverse geography of the United States and over various crops and environmental conditions to better understand and model and ultimately devise a solution for the challenges to the FEWS nexus. One of the intended outcomes of the workshop was to generate a report that will chart the research challenges and opportunities for solving these challenges and have an impact on scientific fields including, sensing technology, hydrology, soil science, climate, data fusion, analysis, visualization, and data driven decision 2 making, as well as agricultural production, local and regional economies, sustainability and planning. The information contained in this post-workshop report serves as that foundation

Pair Analytics: Capturing Reasoning Processes in Collaborative Visual Analytics

Studying how humans interact with abstract, visual representations of massive amounts of data provides knowledge about how cognition works in visual analytics. This knowledge provides guidelines for cognitive-aware design and evaluation of visual analytic tools. Different methods have been used to capture and conceptualize these processes including protocol analysis, experiments, cognitive task analysis, and field studies. In this article, we introduce Pair Analytics: a method for capturing reasoning processes in visual analytics. We claim that Pair Analytics offers two advantages with respect to other methods: (1) a more natural way of making explicit and capturing reasoning processes and (2) an approach to capture social and cognitive processes used to conduct collaborative analysis in real-life settings. We support and illustrate these claims with a pilot study of three phenomena in collaborative visual analytics: coordination of attention, cognitive workload, and navigation of analysis

An Information Theory-Based Approach to Assessing Spatial Patterns in Complex Systems

Given the intensity and frequency of environmental change, the linked and cross-scale nature of social-ecological systems, and the proliferation of big data, methods that can help synthesize complex system behavior over a geographical area are of great value. Fisher information evaluates order in data and has been established as a robust and effective tool for capturing changes in system dynamics, including the detection of regimes and regime shifts. The methods developed to compute Fisher information can accommodate multivariate data of various types and requires no a priori decisions about system drivers, making it a unique and powerful tool. However, the approach has primarily been used to evaluate temporal patterns. In its sole application to spatial data, Fisher information successfully detected regimes in terrestrial and aquatic systems over transects. Although the selection of adjacently positioned sampling stations provided a natural means of ordering the data, such an approach limits the types of questions that can be answered in a spatial context. Here, we expand the approach to develop a method for more fully capturing spatial dynamics. The results reflect changes in the index that correspond with geographical patterns and demonstrate the utility of the method in uncovering hidden spatial trends in complex systems

Using Social Media Websites to Support Scenario-Based Design of Assistive Technology

Indiana University-Purdue University Indianapolis (IUPUI)Having representative users, who have the targeted disability, in accessibility studies is vital to the validity of research findings. Although it is a widely accepted tenet in the HCI community, many barriers and difficulties make it very resource-demanding for accessibility researchers to recruit representative users. As a result, researchers recruit non-representative users, who do not have the targeted disability, instead of representative users in accessibility studies. Although such an approach has been widely justified, evidence showed that findings derived from non-representative users could be biased and even misleading. To address this problem, researchers have come up with different solutions such as building pools of users to recruit from. But still, the data is not widely available and needs a lot of effort and resource to build and maintain. On the other hand, online social media websites have become popular in the last decade. Many online communities have emerged that allow online users to discuss health-related subjects, exchange useful information, or provide emotional support. A large amount of data accumulated in such online communities have gained attention from researchers in the healthcare domain. And many researches have been done based on data from social media websites to better understand health problems to improve the wellbeing of people. Despite the increasing popularity, the value of data from social media websites for accessibility research remains untapped. Hence, my work aims to create methods that could extract valuable information from data collected on social media websites for accessibility practitioners to support their design process. First, I investigate methods that enable researchers to effectively collect representative data from social media websites. More specifically, I look into machine learning approaches that could allow researchers to automatically identify online users who have disabilities (representative users). Second, I investigate methods that could extract useful information from user-generated free-text using techniques drawn from the information extraction domain. Last, I explore how such information should be visualized and presented for designers to support the scenario-based design process in accessibility studies

A Pattern Approach to Examine the Design Space of Spatiotemporal Visualization

Pattern language has been widely used in the development of visualization systems. This dissertation applies a pattern language approach to explore the design space of spatiotemporal visualization. The study provides a framework for both designers and novices to communicate, develop, evaluate, and share spatiotemporal visualization design on an abstract level. The touchstone of the work is a pattern language consisting of fifteen design patterns and four categories. In order to validate the design patterns, the researcher created two visualization systems with this framework in mind. The first system displayed the daily routine of human beings via a polygon-based visualization. The second system showed the spatiotemporal patterns of co-occurring hashtags with a spiral map, sunburst diagram, and small multiples. The evaluation results demonstrated the effectiveness of the proposed design patterns to guide design thinking and create novel visualization practices

Visualization in environmental science

The VISualization of Terrestrial and Aquatic Systems (VISTAS) software development project began with the proposition that visualization would increase the ability of scientists to explore and communicate their data, especially complex datasets that span multiple spatial and temporal scales. A case study of VISTAS articulates how and why scientists intend to use visualization. Scientists intend to use visualization for both confirmatory and exploratory analysis, and as a screening tool, prior to delving into the details of what looks to be interesting patterns in the data. They intend to use visualization to communicate the results of their research in various settings. Visualization effectively conveys changes on the landscape to different stakeholders of varying levels of expertise. Visualization allows researchers and stakeholders to better understand interactions on the landscape. Visualization not only shows the data, but also helps people draw conclusions. Ultimately, scientists are planning to design and create their own visualizations once the VISTAS project is over. The case study analyses also articulate a number of unanswered research questions regarding the technical training and research partnerships of scientists, their access to software tools, and how emerging research problems might be approached in environmental science. These unanswered questions challenge a visualization-centric bias found in similar research and development projects and seek to increase the benefits of visualization while filling in the gaps where visualization is limited

Scale and complexity in visual analytics

The fundamental problem that we face is that a variety of large-scale problems in security, public safety, energy, ecology, health care and basic science all require that we process and understand increasingly vast amounts and variety of data. There is a growing impedance mismatch between data size/complexity and the human ability to understand and interact with data. Visual analytic tools are intended to help reduce that impedance mismatch by using analytic tools to reduce the amount of data that must be viewed, and visualization tools to help understand the patterns and relationships in the reduced data. But visual analytic tools must address a variety of scalability issues if they are to succeed. In this paper, we characterize the scalability and complexity issues in visual analytics. We discuss some highlights on progress that has been made in the past 5 years, as well as key areas where more progress is needed