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Department of Computer Science and EngineeringMany visualization systems have provided multiple coordinated views (MCVs) with a belief that using MCVs brings benefits during visual analysis. However, if a tool requires tedious or repeated interactions to create one view, users may feel difficulty in utilizing the MCV tools due to perceived expensive interaction costs. To reduce such interaction costs, a number of visual tools have started providing a method, called visualization duplication to allow users to copy an existing visualization with one click. In spite of the importance of such easy view creation method, very little empirical work exists on measuring impacts of the method. In this work, we aim to investigate the impacts of visualization duplication on visual analysis strategies, interaction behaviors, and analysis performance. To achieve the goals, we designed a prototype visual tool, equipped with the easy view creation method and conducted a human-subjects study. In the experiment, 44 participants completed five analytic tasks using a visualization system. Through quantitative and qualitative analysis, we discovered that visualization duplication is related to the number of views and generated insights and accuracy of visual analysis. The results also revealed visualization duplication effects on deciding analytical strategies and interaction patterns.clos

The Influence of Visual Provenance Representations on Strategies in a Collaborative Hand-off Data Analysis Scenario

Conducting data analysis tasks rarely occur in isolation. Especially in intelligence analysis scenarios where different experts contribute knowledge to a shared understanding, members must communicate how insights develop to establish common ground among collaborators. The use of provenance to communicate analytic sensemaking carries promise by describing the interactions and summarizing the steps taken to reach insights. Yet, no universal guidelines exist for communicating provenance in different settings. Our work focuses on the presentation of provenance information and the resulting conclusions reached and strategies used by new analysts. In an open-ended, 30-minute, textual exploration scenario, we qualitatively compare how adding different types of provenance information (specifically data coverage and interaction history) affects analysts' confidence in conclusions developed, propensity to repeat work, filtering of data, identification of relevant information, and typical investigation strategies. We see that data coverage (i.e., what was interacted with) provides provenance information without limiting individual investigation freedom. On the other hand, while interaction history (i.e., when something was interacted with) does not significantly encourage more mimicry, it does take more time to comfortably understand, as represented by less confident conclusions and less relevant information-gathering behaviors. Our results contribute empirical data towards understanding how provenance summarizations can influence analysis behaviors.Comment: to be published in IEEE Vis 202

Integrating E-Commerce and Data Mining: Architecture and Challenges

We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based on our expe-rience at Blue Martini Software, for supporting this integration. The architecture can dramatically reduce the pre-processing, cleaning, and data understanding effort often documented to take 80% of the time in knowledge discovery projects. We emphasize the need for data collection at the application server layer (not the web server) in order to support logging of data and metadata that is essential to the discovery process. We describe the data transformation bridges required from the transaction processing systems and customer event streams (e.g., clickstreams) to the data warehouse. We detail the mining workbench, which needs to provide multiple views of the data through reporting, data mining algorithms, visualization, and OLAP. We con-clude with a set of challenges.Comment: KDD workshop: WebKDD 200

Quantifying, Modeling and Managing How People Interact with Visualizations on the Web

The growing number of interactive visualizations on the web has made it possible for the general public to access data and insights that were once only available to domain experts. At the same time, this rise has yielded new challenges for visualization creators, who must now understand and engage a growing and diverse audience. To bridge this gap between creators and audiences, we explore and evaluate components of a design-feedback loop that would enable visualization creators to better accommodate their audiences as they explore the visualizations. In this dissertation, we approach this goal by quantifying, modeling and creating tools that manage people’s open-ended explorations of visualizations on the web. In particular, we: 1. Quantify the effects of design alternatives on people’s interaction patterns in visualizations. We define and evaluate two techniques: HindSight (encoding a user’s interaction history) and text-based search, where controlled experiments suggest that design details can significantly modulate the interaction patterns we observe from participants using a given visualization. 2. Develop new metrics that characterize facets of people’s exploration processes. Specifically, we derive expressive metrics describing interaction patterns such as exploration uniqueness, and use Bayesian inference to model distributional effects on interaction behavior. Our results show that these metrics capture novel patterns in people’s interactions with visualizations. 3. Create tools that manage and analyze an audience’s interaction data for a given visualization. We develop a prototype tool, ReVisIt, that visualizes an audience’s interactions with a given visualization. Through an interview study with visualization creators, we found that ReVisIt make creators aware of individual and overall trends in their audiences’ interaction patterns. By establishing some of the core elements of a design-feedback loop for visualization creators, the results in this research may have a tangible impact on the future of publishing interactive visualizations on the web. Equipped with techniques, metrics, and tools that realize an initial feedback loop, creators are better able to understand the behavior and user needs, and thus create visualizations that make data and insights more accessible to the diverse audiences on the web

Active tag recommendation for interactive entity search : Interaction effectiveness and retrieval performance

We introduce active tag recommendation for interactive entity search, an approach that actively learns to suggest tags from preceding user interactions with the recommended tags. The approach utilizes an online reinforcement learning model and observes user interactions on the recommended tags to reward or penalize the model. Active tag recommendation is implemented as part of a realistic search engine indexing a large collection of movie data. The approach is evaluated in task-based user experiments comparing a complete search system enhanced with active tag recommendation to a control system in which active tag recommendation is not available. In the experiment, participants (N = 45) performed search tasks on the movie domain and the corresponding search interactions, information selections, and entity rankings were logged and analyzed. The results show that active tag recommendation (1) improves the ranking of entities compared to written-query interaction, (2) increases the amount of interaction and effectiveness of interactions to rank entities that end up being selected in a task, and (3) reduces, but does not substitute, the need for written-query interaction (4) without compromising task execution time. The results imply that active learning for search support can help users to interact with entity search systems by reducing the need for writing queries and improve search outcomes without compromising the time used for searching.Peer reviewe

Evaluating Visual Data Analysis Systems: A Discussion Report

International audienceVisual data analysis is a key tool for helping people to make sense of and interact with massive data sets. However, existing evaluation methods (e.g., database benchmarks, individual user studies) fail to capture the key points that make systems for visual data analysis (or visual data systems) challenging to design. In November 2017, members of both the Database and Visualization communities came together in a Dagstuhl seminar to discuss the grand challenges in the intersection of data analysis and interactive visualization. In this paper, we report on the discussions of the working group on the evaluation of visual data systems, which addressed questions centered around developing better evaluation methods, such as " How do the different communities evaluate visual data systems? " and " What we could learn from each other to develop evaluation techniques that cut across areas? ". In their discussions, the group brainstormed initial steps towards new joint evaluation methods and developed a first concrete initiative — a trace repository of various real-world workloads and visual data systems — that enables researchers to derive evaluation setups (e.g., performance benchmarks, user studies) under more realistic assumptions, and enables new evaluation perspectives (e.g., broader meta analysis across analysis contexts, reproducibility and comparability across systems)

Exploring knowledge learning in collaborative information seeking process

Knowledge learning is recognized as an important component in people's search process. Existing studies on this topic usually measure the knowledge growth before and after a search. However, there still lacks a fine-grained understanding of users' knowledge change patterns within a search process and users' adoption of different sources for learning. In this on-going project, we are exploring answers to both questions in collaborative information seeking (CIS) since the CIS tasks are usually exploratory, which triggers learning, and involve diverse learning resources such as self-explored search content, partners' search content and explicit communication between them. Through analyzing the data from a controlled laboratory user study with both collaborative and individual information seeking conditions, we demonstrated that users' knowledge keeps growing in both conditions, but they issue significantly more diverse queries in the collaborative condition. Our analysis of users' queries also revealed that the adoption of different learning resources varies at different information seeking stages, and the adoption is influenced by the nature of search tasks too. Finally, we propose several insights for system design to enhance knowledge learning in collaborative information seeking process