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

Designing for Cross-Device Interactions

Driven by technological advancements, we now own and operate an ever-growing number of digital devices, leading to an increased amount of digital data we produce, use, and maintain. However, while there is a substantial increase in computing power and availability of devices and data, many tasks we conduct with our devices are not well connected across multiple devices. We conduct our tasks sequentially instead of in parallel, while collaborative work across multiple devices is cumbersome to set up or simply not possible. To address these limitations, this thesis is concerned with cross-device computing. In particular it aims to conceptualise, prototype, and study interactions in cross-device computing. This thesis contributes to the field of Human-Computer Interaction (HCI)—and more specifically to the area of cross-device computing—in three ways: first, this work conceptualises previous work through a taxonomy of cross-device computing resulting in an in-depth understanding of the field, that identifies underexplored research areas, enabling the transfer of key insights into the design of interaction techniques. Second, three case studies were conducted that show how cross-device interactions can support curation work as well as augment users’ existing devices for individual and collaborative work. These case studies incorporate novel interaction techniques for supporting cross-device work. Third, through studying cross-device interactions and group collaboration, this thesis provides insights into how researchers can understand and evaluate multi- and cross-device interactions for individual and collaborative work. We provide a visualization and querying tool that facilitates interaction analysis of spatial measures and video recordings to facilitate such evaluations of cross-device work. Overall, the work in this thesis advances the field of cross-device computing with its taxonomy guiding research directions, novel interaction techniques and case studies demonstrating cross-device interactions for curation, and insights into and tools for effective evaluation of cross-device systems

Supporting the sensemaking process in visual analytics

Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. It involves interactive exploration of data using visualizations and automated data analysis to gain insight, and to ultimately make better decisions. It aims to support the sensemaking process in which information is collected, organized and analyzed to form new knowledge and inform further action. Interactive visual exploration of the data can lead to many discoveries in terms of relations, patterns, outliers and so on. It is difficult for the human working memory to keep track of all findings during a visual analysis. Also, synthesis of many different findings and relations between those findings increase the information overload and thereby hinders the sensemaking process further. The central theme of this dissertation is How to support users in their sensemaking process during interactive exploration of data? To support the sensemaking process in visual analytics, we mainly focus on how to support users to capture, reuse, review, share, and present the key aspects of interest concerning the analysis process and the findings during interactive exploration of data. For this, we have developed generic models and tools that enable users to capture findings with provenance, and construct arguments; and to review, revise and share their visual analysis. First, we present a sensemaking framework for visual analytics that contains three linked views: a data view, a navigation view and a knowledge view for supporting the sense-making process. The data view offers interactive data visualization tools. The navigation view automatically captures the interaction history using a semantically rich action model and provides an overview of the analysis structure. The knowledge view is a basic graphics editor that helps users to record findings with provenance and to organize findings into claims using diagramming techniques. Users can exploit automatically captured interaction history and manually recorded findings to review and revise their visual analysis. Thus, the analysis process can be archived and shared with others for collaborative visual analysis. Secondly, we enable analysts to capture data selections as semantic zones during an analysis, and to reuse these zones on different subsets of data. We present a Select & Slice table that helps analysts to capture, manipulate, and reuse these zones more explicitly during exploratory data analysis. Users can reuse zones, combine zones, and compare and trace items of interest across different semantic zones and data slices. Finally, exploration overviews and searching techniques based on keywords, content similarity, and context helped analysts to develop awareness over the key aspects of the exploration concerning the analysis process and findings. On one hand, they can proactively search analysis processes and findings for reviewing purposes. On the other hand, they can use the system to discover implicit connections between findings and the current line of inquiry, and recommend these related findings during an interactive data exploration. We implemented the models and tools described in this dissertation in Aruvi and HARVEST. Using Aruvi and HARVEST, we studied the implications of these models on a user’s sensemaking process. We adopted the short-term and long-term case studies approach to study support offered by these tools for the sensemaking process. The observations of the case studies were used to evaluate the models

Toward Systematic Design Considerations of Organizing Multiple Views

Multiple-view visualization (MV) has been used for visual analytics in various fields (e.g., bioinformatics, cybersecurity, and intelligence analysis). Because each view encodes data from a particular perspective, analysts often use a set of views laid out in 2D space to link and synthesize information. The difficulty of this process is impacted by the spatial organization of these views. For instance, connecting information from views far from each other can be more challenging than neighboring ones. However, most visual analysis tools currently either fix the positions of the views or completely delegate this organization of views to users (who must manually drag and move views). This either limits user involvement in managing the layout of MV or is overly flexible without much guidance. Then, a key design challenge in MV layout is determining the factors in a spatial organization that impact understanding. To address this, we review a set of MV-based systems and identify considerations for MV layout rooted in two key concerns: perception, which considers how users perceive view relationships, and content, which considers the relationships in the data. We show how these allow us to study and analyze the design of MV layout systematically.Comment: Short paper with 4 pages + 1 reference page, 2 figures, 1 table, accepted at IEEE VIS 2022 conferenc

Prompting for Discovery: Flexible Sense-Making for AI Art-Making with Dreamsheets

Design space exploration (DSE) for Text-to-Image (TTI) models entails navigating a vast, opaque space of possible image outputs, through a commensurately vast input space of hyperparameters and prompt text. Minor adjustments to prompt input can surface unexpectedly disparate images. How can interfaces support end-users in reliably steering prompt-space explorations towards interesting results? Our design probe, DreamSheets, supports exploration strategies with LLM-based functions for assisted prompt construction and simultaneous display of generated results, hosted in a spreadsheet interface. The flexible layout and novel generative functions enable experimentation with user-defined workflows. Two studies, a preliminary lab study and a longitudinal study with five expert artists, revealed a set of strategies participants use to tackle the challenges of TTI design space exploration, and the interface features required to support them - like using text-generation to define local "axes" of exploration. We distill these insights into a UI mockup to guide future interfaces.Comment: 13 pages, 14 figures, currently under revie

Investigating Practices When Using an Overview Device in Collaborative Multi-Surface Trip-Planning

The availability of mobile device ecologies enables new types of ad-hoc co-located decision-making and sensemak-ing practices in which people find, collect, discuss, and share information. However, little is known about what kind of device configurations are suitable for these types of tasks. This paper contributes new insights into how people use configurations of devices for one representative example task: collaborative co-located trip-planning. We present an empirical study that explores and compares three strategies to use multiple devices: no-overview, overview on own device, and a separate overview device. The results show that the overview facilitated decision- and sensemaking during a collaborative trip-planning task by aiding groups to iterate their itinerary, organize locations and timings efficiently, and discover new insights. Groups shared and discussed more opinions, resulting in more democratic decision-making. Groups provided with a separate overview device engaged more frequently and spent more time in closely-coupled collaboration

Data, Data Everywhere, and Still Too Hard to Link: Insights from User Interactions with Diabetes Apps

For those with chronic conditions, such as Type 1 diabetes, smartphone apps offer the promise of an affordable, convenient, and personalized disease management tool. How- ever, despite significant academic research and commercial development in this area, diabetes apps still show low adoption rates and underwhelming clinical outcomes. Through user-interaction sessions with 16 people with Type 1 diabetes, we provide evidence that commonly used interfaces for diabetes self-management apps, while providing certain benefits, can fail to explicitly address the cognitive and emotional requirements of users. From analysis of these sessions with eight such user interface designs, we report on user requirements, as well as interface benefits, limitations, and then discuss the implications of these findings. Finally, with the goal of improving these apps, we identify 3 questions for designers, and review for each in turn: current shortcomings, relevant approaches, exposed challenges, and potential solutions

TOME: Interactive TOpic Model and MEtadata Visualization

As archives are being digitized at an increasing rate, scholars will require new tools to make sense of this expanding amount of material. We propose to build TOME, a tool to support the interactive exploration and visualization of text-based archives. Drawing upon the technique of topic modeling--a computational method for identifying themes that recur across a collection--TOME will visualize the topics that characterize each archive, as well as the relationships between specific topics and related metadata, such as publication date. An archive of 19th-century antislavery newspapers, characterized by diverse authors and shifting political alliances, will serve as our initial dataset; it promises to motivate new methods for visualizing topic models and extending their impact. In turn, by applying our new methods to these texts, we will illuminate how issues of gender and racial identity affect the development of political ideology in the nineteenth century, and into the present day