Temporal Summary Images: An Approach to Narrative Visualization via Interactive Annotation Generation and Placement

Bryan, Chris; Ma, Kwan‐Liu; Woodring, Jonathan

doi:10.1109/tvcg.2016.2598876

Cited by 79 publications

(46 citation statements)

References 35 publications

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“…Contextifier [16] produces annotated visualizations of stock behavior using news articles about a company. The system described by Bryan et al [5] helps users in finding points of interest and provides a workflow for annotating the data.…”

Section: Bringing the Human In The Loopmentioning

confidence: 99%

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Taxonomizer: Interactive Construction of Fully Labeled Hierarchical Groupings from Attributes of Multivariate Data

Mahmood

Mueller

2020

IEEE Trans. Visual. Comput. Graphics

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Figure 1: The housing dataset for Kings County displayed using Taxonomizer. The user interface consists of six coordinated views. (a) The semantic space. (b) The data space. (c) The hierarchy built from combining information spaces (a) and (b). (d) The cophenetic correlation plot which allows users to specify the weight of (a) and (b) to generate (c). (e) The control panel gives the user various options to manipulate the structure of the hierarchy. (f) The word suggestion panel gives suggestions for labeling the nodes of the hierarchy.Abstract-Organizing multivariate data spaces by their dimensions or attributes can be a rather difficult task. Most of the work in this area focuses on the statistical aspects such as correlation clustering, dimension reduction, and the like. These methods typically produce hierarchies in which the leaf nodes are labeled by the attribute names while the inner nodes are often represented by just a statistical measure and criterion, such as a threshold. This makes them difficult to understand for mainstream users. Taxonomies in science, biology, engineering, etc. on the other hand, are easy to comprehend since they provide meaningful labels at the inner nodes as well. Labeling inner nodes of taxonomies automatically requires the identification of hypernyms. Our proposed framework, called Taxonomizer, takes a visual analytics approach to meet this challenge. It appeals to the wisdom of humans to liaise with state of the art data analytics, neural word embeddings, and lexical databases. It consists of a set of visual tools that starts out with an automatically computed hierarchy where the leaf nodes are the original data attributes, and it then allows users to sculpt high-quality taxonomies for any multivariate dataset.

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Section: Bringing the Human In The Loopmentioning

confidence: 99%

“…We follow Jane who has compiled some data on student performance in secondary education 5 . She wants to create a web tool that allows users to visualize the data and decides to use Taxonomizer to create a hierarchical structure where users can explore the data at various levels of granularity.…”

Section: Usage Scenariomentioning

confidence: 99%

Taxonomizer: Interactive Construction of Fully Labeled Hierarchical Groupings from Attributes of Multivariate Data

Mahmood

Mueller

2020

IEEE Trans. Visual. Comput. Graphics

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“…A common combination used to tell stories with more context utilizes numeric and textual data. Textual data are often overlaid on traditional numeric visualization techniques as (interactive) annotations [BMW17, HDA13] to give reasons behind the numeric data behavior. The approach is more effective when displaying a comparatively smaller amount of contextual data, as it can suffer from scalability issues.…”

Section: Related Workmentioning

confidence: 99%

Bridging the Data Analysis Communication Gap Utilizing a Three‐Component Summarized Line Graph

Yau

Karimzadeh

Surakitbanharn

et al. 2019

Computer Graphics Forum

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Communication‐minded visualizations are designed to provide their audience—managers, decision‐makers, and the public—with new knowledge. Authoring such visualizations effectively is challenging because the audience often lacks the expertise, context, and time that professional analysts have at their disposal to explore and understand datasets. We present a novel summarized line graph visualization technique designed specifically for data analysts to communicate data to decision‐makers more effectively and efficiently. Our summarized line graph reduces a large and detailed dataset of multiple quantitative time‐series into (1) representative data that provides a quick takeaway of the full dataset; (2) analytical highlights that distinguish specific insights of interest; and (3) a data envelope that summarizes the remaining aggregated data. Our summarized line graph achieved the best overall results when evaluated against line graphs, band graphs, stream graphs, and horizon graphs on four representative tasks.

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“…Variations in data can, to some extent, be detected automatically with no domain knowledge. Progress in this area has recently been made in InfoVis to automatically annotate data points of interest in line charts, stacked graphs, and alluvial diagrams (Bryan, Ma, & Woodring, 2017); and in indoor maps (Metoyer, Zhi, Janczuk, & Scheirer, 2018). However, although specific properties of lines/curves (e.g., peaks and troughs, intersections, gradients), and time-series data (e.g., three consecutive low-performance episodes in a particular session) can be used by the system to highlight a multitude of changes in a graph, from an educational point of view, these may also be irrelevant.…”

Section: A Typical Exploratory Chartmentioning

confidence: 99%

Exploratory versus Explanatory Visual Learning Analytics: Driving Teachers’ Attention through Educational Data Storytelling

et al. 2018

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From a human-centred computing perspective, supporting the interpretation of educational dashboards and visualizations by the people intended to use them exposes critical design challenges that may often be trivialized. Empirical evidence already shows that "usable" visualizations are not necessarily effective from an educational perspective. Since an educator's interpretation of visualized data is essentially the construction of a narrative about student progress, in this paper, we propose the concept of "Educational Data Storytelling" as an approach for explaining student data by aligning educational visualizations with the intended learning design. We present a pilot study that explores the effectiveness of these data storytelling elements based on educator responses to prototypes by analyzing the kinds of stories they articulate, their eye-tracking behaviour, and their preferences after inspecting a series of student data visualizations. The dual purpose is to understand the contribution of each visual element for data storytelling, as well as the effectiveness of the enhancements when combined. 1 Notes for Practice  The learning analytics field, to effectively support teaching and learning visually, needs to move on from visual analytics that invite users to explore the data, to visualizations that explain insights.  In this paper, we propose the concept of "Educational Data Storytelling" as an approach to designing visual learning analytics interfaces that explain student data by aligning educational visualizations with the learning design intended by the teacher.  We see the potential of learning-design-driven data storytelling elements to support sensemaking by guiding students and teachers to "one learning story per visualization," given that learning is a complex task.

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Temporal Summary Images: An Approach to Narrative Visualization via Interactive Annotation Generation and Placement

Cited by 79 publications

References 35 publications

Taxonomizer: Interactive Construction of Fully Labeled Hierarchical Groupings from Attributes of Multivariate Data

Taxonomizer: Interactive Construction of Fully Labeled Hierarchical Groupings from Attributes of Multivariate Data

Bridging the Data Analysis Communication Gap Utilizing a Three‐Component Summarized Line Graph

Exploratory versus Explanatory Visual Learning Analytics: Driving Teachers’ Attention through Educational Data Storytelling

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