You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems

Visual query systems (VQSs) empower users to interactively search for line charts with desired visual patterns, typically specified using intuitive sketch-based interfaces. Despite decades of past work on VQSs, these efforts have not translated to adoption in practice, possibly because VQSs are largely evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we collaborated with experts from three diverse domains---astronomy, genetics, and material science---via a year-long user-centered design process to develop a VQS that supports their workflow and analytical needs, and evaluate how VQSs can be used in practice. Our study results reveal that ad-hoc sketch-only querying is not as commonly used as prior work suggests, since analysts are often unable to precisely express their patterns of interest. In addition, we characterize three essential sensemaking processes supported by our enhanced VQS. We discover that participants employ all three processes, but in different proportions, depending on the analytical needs in each domain. Our findings suggest that all three sensemaking processes must be integrated in order to make future VQSs useful for a wide range of analytical inquiries.Comment: Accepted for presentation at IEEE VAST 2019, to be held October 20-25 in Vancouver, Canada. Paper will also be published in a special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG) IEEE VIS (InfoVis/VAST/SciVis) 2019 ACM 2012 CCS - Human-centered computing, Visualization, Visualization design and evaluation method

Learning what matters - Sampling interesting patterns

In the field of exploratory data mining, local structure in data can be described by patterns and discovered by mining algorithms. Although many solutions have been proposed to address the redundancy problems in pattern mining, most of them either provide succinct pattern sets or take the interests of the user into account-but not both. Consequently, the analyst has to invest substantial effort in identifying those patterns that are relevant to her specific interests and goals. To address this problem, we propose a novel approach that combines pattern sampling with interactive data mining. In particular, we introduce the LetSIP algorithm, which builds upon recent advances in 1) weighted sampling in SAT and 2) learning to rank in interactive pattern mining. Specifically, it exploits user feedback to directly learn the parameters of the sampling distribution that represents the user's interests. We compare the performance of the proposed algorithm to the state-of-the-art in interactive pattern mining by emulating the interests of a user. The resulting system allows efficient and interleaved learning and sampling, thus user-specific anytime data exploration. Finally, LetSIP demonstrates favourable trade-offs concerning both quality-diversity and exploitation-exploration when compared to existing methods.Comment: PAKDD 2017, extended versio

IMPACT Concept of Operations

NASAs future exploration missions mandate a significant paradigm change for mission planning, spacecraft design, human systems integration, and in-flight medical care due to constraints on mass, volume, power, resupply missions, and medical evacuation capabilities. These constraints require further development of the human health and performance system, which includes the medical, task performance, wellness, data, human and other systems necessary to keep the crew healthy and functioning optimally. The human health and performance system will be tightly integrated with mission and habitat design to provide a sufficient human health and performance infrastructure to enable mission success. A suite of systems engineering tools will aid in the decision making process for the development of such a human health and performance system. This Concept of Operations provides a vision for a tool suite to conduct evaluations of human health and performance system options, inform research prioritization, and provide trade study support, based on evidence, risks, and systems engineering principles. The integrated tool suite under development is IMPACT

Data Visualization for Network Simulations

As many other kinds of simulation experiments, simulations of computer networks tend to generate high volumes of output data. While the collection and the statistical processing of these data are challenges in and of themselves, creating meaningful visualizations from them is as much an art as it is a science. A sophisticated body of knowledge in information design and data visualization has been developed and continues to evolve. However, many of the visualizations created by the network simulation community tend to be less than optimal at creating compelling, informative narratives from experimental output data. The primary contribution of this paper is to explore some of the design dimensions in visualization and some advances in the field that are applicable to network simulation. We also discuss developments in the creation of the visualization subsystem in the Simulation Automation Framework for Experiments (SAFE) in the context of best practices for data visualization

An Evaluation-Guided Approach for Effective Data Visualization on Tablets

There is a rising trend of data analysis and visualization tasks being performed on a tablet device. Apps with interactive data visualization capabilities are available for a wide variety of domains. We investigate whether users grasp how to effectively interpret and interact with visualizations. We conducted a detailed user evaluation to study the abilities of individuals with respect to analyzing data on a tablet through an interactive visualization app. Based upon the results of the user evaluation, we find that most subjects performed well at understanding and interacting with simple visualizations, specifically tables and line charts. A majority of the subjects struggled with identifying interactive widgets, recognizing interactive widgets with overloaded functionality, and understanding visualizations which do not display data for sorted attributes. Based on our study, we identify guidelines for designers and developers of mobile data visualization apps that include recommendations for effective data representation and interaction

Convo: What does conversational programming need? An exploration of machine learning interface design

Vast improvements in natural language understanding and speech recognition have paved the way for conversational interaction with computers. While conversational agents have often been used for short goal-oriented dialog, we know little about agents for developing computer programs. To explore the utility of natural language for programming, we conducted a study ($n$=45) comparing different input methods to a conversational programming system we developed. Participants completed novice and advanced tasks using voice-based, text-based, and voice-or-text-based systems. We found that users appreciated aspects of each system (e.g., voice-input efficiency, text-input precision) and that novice users were more optimistic about programming using voice-input than advanced users. Our results show that future conversational programming tools should be tailored to users' programming experience and allow users to choose their preferred input mode. To reduce cognitive load, future interfaces can incorporate visualizations and possess custom natural language understanding and speech recognition models for programming.Comment: 9 pages, 7 figures, submitted to VL/HCC 2020, for associated user study video: https://youtu.be/TC5P3OO5ex