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

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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

Clear Visual Separation of Temporal Event Sequences

Extracting and visualizing informative insights from temporal event sequences becomes increasingly difficult when data volume and variety increase. Besides dealing with high event type cardinality and many distinct sequences, it can be difficult to tell whether it is appropriate to combine multiple events into one or utilize additional information about event attributes. Existing approaches often make use of frequent sequential patterns extracted from the dataset, however, these patterns are limited in terms of interpretability and utility. In addition, it is difficult to assess the role of absolute and relative time when using pattern mining techniques. In this paper, we present methods that addresses these challenges by automatically learning composite events which enables better aggregation of multiple event sequences. By leveraging event sequence outcomes, we present appropriate linked visualizations that allow domain experts to identify critical flows, to assess validity and to understand the role of time. Furthermore, we explore information gain and visual complexity metrics to identify the most relevant visual patterns. We compare composite event learning with two approaches for extracting event patterns using real world company event data from an ongoing project with the Danish Business Authority.Comment: In Proceedings of the 3rd IEEE Symposium on Visualization in Data Science (VDS), 201

Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis

In interactive data analysis processes, the dialogue between the human and the computer is the enabling mechanism that can lead to actionable observations about the phenomena being investigated. It is of paramount importance that this dialogue is not interrupted by slow computational mechanisms that do not consider any known temporal human-computer interaction characteristics that prioritize the perceptual and cognitive capabilities of the users. In cases where the analysis involves an integrated computational method, for instance to reduce the dimensionality of the data or to perform clustering, such non-optimal processes are often likely. To remedy this, progressive computations, where results are iteratively improved, are getting increasing interest in visual analytics. In this paper, we present techniques and design considerations to incorporate progressive methods within interactive analysis processes that involve high-dimensional data. We define methodologies to facilitate processes that adhere to the perceptual characteristics of users and describe how online algorithms can be incorporated within these. A set of design recommendations and according methods to support analysts in accomplishing high-dimensional data analysis tasks are then presented. Our arguments and decisions here are informed by observations gathered over a series of analysis sessions with analysts from finance. We document observations and recommendations from this study and present evidence on how our approach contribute to the efficiency and productivity of interactive visual analysis sessions involving high-dimensional data

Characterizing the Quality of Insight by Interactions: A Case Study

Understanding the quality of insight has become increasingly important with the trend of allowing users to post comments during visual exploration, yet approaches for qualifying insight are rare. This article presents a case study to investigate the possibility of characterizing the quality of insight via the interactions performed. To do this, we devised the interaction of a visualization tool—MediSyn—for insight generation. MediSyn supports five types of interactions: selecting, connecting, elaborating, exploring, and sharing. We evaluated MediSyn with 14 participants by allowing them to freely explore the data and generate insights. We then extracted seven interaction patterns from their interaction logs and correlated the patterns to four aspects of insight quality. The results show the possibility of qualifying insights via interactions. Among other findings, exploration actions can lead to unexpected insights; the drill-down pattern tends to increase the domain values of insights. A qualitative analysis shows that using domain knowledge to guide exploration can positively affect the domain value of derived insights. We discuss the study’s implications, lessons learned, and future research opportunities.Peer reviewe

What is Interaction for Data Visualization?

International audienceInteraction is fundamental to data visualization, but what "interaction" means in the context of visualization is ambiguous and confusing. We argue that this confusion is due to a lack of consensual definition. To tackle this problem, we start by synthesizing an inclusive view of interaction in the visualization community-including insights from information visualization, visual analytics and scientific visualization, as well as the input of both senior and junior visualization researchers. Once this view takes shape, we look at how interaction is defined in the field of human-computer interaction (HCI). By extracting commonalities and differences between the views of interaction in visualization and in HCI, we synthesize a definition of interaction for visualization. Our definition is meant to be a thinking tool and inspire novel and bolder interaction design practices. We hope that by better understanding what interaction in visualization is and what it can be, we will enrich the quality of interaction in visualization systems and empower those who use them

ANALYZING USER INTERACTION LOGS OF AN EDUCATIONAL VISUALIZATION SYSTEM TO UNDERSTAND HOW STUDENTS GENERATE INSIGHTS

Department of Computer Science and EngineeringVisual analytics systems have been becoming popular in many domains. Recently, a visual analytical tool, VAiRoma is designed in educational domain to support students learn the history class. However, how users are interacting with such systems is still not known enough. In an educational domain, it is important to know how users are gaining insights. It may give us an opportunity to understand the user???s learning style, so that we can design better visualization tools in the future. In this thesis, I will analyze the interaction logs of an educational visualization system, VAiRoma, in order to explore how users generating insights via the system. Based on the results, users tried more explorative interactions at the initial stages of their insight generation path. In the middle of the path, users mostly read some textual information. Toward the end, they attempted to show their understandings from what they learnt by creating an annotation. There is also a cyclic behavior of an insight generation path. In 38% of cases, during the annotation creation process, the users cancelled to ???create an annotation??? and went back to read some textual information.ope