Interactive dynamics for visual analysis

Heer, Jeffrey; Shneiderman, Ben

doi:10.1145/2133806.2133821

Cited by 314 publications

(252 citation statements)

References 22 publications

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“…Exponential growth in data size and complexity and increased reliance on heterogeneous, third-party resources impact human ability to effectively extract, manage and make use of the knowledge content of the vast amounts of data that feed into task completion in today's data-rich and data-driven economy [16,[25][26][27]. Visual analytics provides a valuable tool that complements advanced human perception and analytical ability with automated data processing and mining.…”

Section: Visual Ontology-guided Exploration and Analysismentioning

confidence: 99%

“…Liu et al, as do Hervás et al [5], Kerren et al [26], Turkay et al [27], highlight the benefits in functionality for (automated) animation and interactive data exploration, that gradually reveal ROIs and trends in data, aiding the construction of understanding of complex, static and dynamic datasets. Inselberg [30], Heer et al [25], among others, look at the benefits in multi-dimensional visualisation techniques such as scatterplot matrices and parallel coordinates. Kulyk et al [31] examine the use of focus+context techniques to aid delving into ROIs in large, complex data.…”

Section: Visual Ontology-guided Exploration and Analysismentioning

confidence: 99%

“…Developing intuitive, user-centred, and therefore also taskcentred, tools for effective visual analysis is not trivial, especially where a wide range of expertise, or lack thereof, is found in target end users [1,25,31]. Designing effective support for complex scenarios and data typically involves the exploration of the impact and use of multiple sub-components [4,12,26,27,29,32], following a process that may involve any or all of the following in an iterative cycle:…”

Section: Visual Ontology-guided Exploration and Analysismentioning

confidence: 99%

“…The user may then proceed to navigate through the data to gain a better understanding of more distant ROIs. While starting with data overviews is the more common case, Heer et al [25], for instance, demonstrate the alternative approach, that uses filters for data reduction and/or sampling in especially large datasets, to start exploration from an ROI. We have incorporated the feedback obtained into additional functionality for data pre-processing and interactive filtering.…”

Section: Countrymentioning

confidence: 99%

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Structuring visual exploratory analysis of skill demand

Dadzie

Sibarani

Novalija

et al. 2018

Journal of Web Semantics

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a b s t r a c tThe analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology-and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users' analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on.

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Section: Visual Ontology-guided Exploration and Analysismentioning

confidence: 99%

Section: Visual Ontology-guided Exploration and Analysismentioning

confidence: 99%

Section: Visual Ontology-guided Exploration and Analysismentioning

confidence: 99%

Section: Countrymentioning

confidence: 99%

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Structuring visual exploratory analysis of skill demand

Dadzie

Sibarani

Novalija

et al. 2018

Journal of Web Semantics

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show abstract

“…The development of the visualisation system as such and of the different types of diagrams was based on the principles of information visualisation [29,30,[33][34][35][36][37][38][39] and was guided by the goal "[…] to amplify cognition by the [...] visual representation of abstract data" [32] . The display of the data rested on general issues of information perception and interpretation as well as on issues of acceptance [30,35,36] .…”

Section: Principles Of Data Visualisation Applied In This Studymentioning

confidence: 99%

Development and evaluation of a three-dimensional multi-level model for visualising the workflow composite score in a health IT benchmark

Thye¹,

Hübner²,

Straede³

et al. 2016

JBEI

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Background: Clinical information logistics is the backbone of care workflows inside and outside of hospitals. Due to the great potential of health IT to support clinical processes its contribution needs to be regularly monitored and governed. IT benchmarks are a well-known instrument to optimise the availability and use of IT by guiding the decision making process. The aim of this study was to translate IT benchmarking results that were grounded on a hierarchical workflow scoring system into an appropriate visualisation concept. Methods:To this end, a three-dimensional multi-level model was developed, which allowed the decomposition of the highly aggregated workflow composite score into score views for the individual clinical workflows concerned and for the descriptors of these workflows. Furthermore this multi-level model helped to break down the score views into single and multiple indicator views. Results:The results could be visualised per hospital in comparison to the results of organisations of similar size and ownership (peer reference groups) and in comparison to different types of innovation adopters. The multi-level model was implemented in a benchmark of 199 hospitals and evaluated by the chief information officers. The evaluation resulted in high ratings for the comprehensibility of the different types of views of the scores and indicators. Conclusions:The implementation of the multi-level model in a large benchmark of hospitals proved to be feasible and useful in terms of the overall structure and the different indicator views. There seems to be a preference for less complex and familiar views.

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Interactive Web‐based Data Visualization and Analysis Tool for Synthetizing on‐farm Research Networks Data

Laurent

Lyu

Kyveryga

et al. 2020

Research Synthesis Methods

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The on-farm research network concept enables a group of farmers to test new agricultural management practices under local conditions with support from local researchers or agronomists. Different on-farm trials based on the same experimental design are conducted over several years and sites to test the effectiveness of different innovative management practices aimed at increasing crop productivity and profitability. As a larger amount of historical trial data are being accumulated, data of all the trials require analyses and summarization. Summaries of on-farm trials are usually presented to farmers as individual field reports, which are not optimal for the dissemination of results and decision making. A more practical communication method is needed to enhance result communication and decision making. R Shiny is a new rapidly developing technology for turning R data analyses into interactive web applications. For the first time for on-farm research networks, we developed and launched an interactive web tool called ISOFAST using R Shiny. ISOFAST simultaneously reports all trial results about the same management practice to simplify interpretation of multi-site and multi-year summaries. We used a random-effects model to synthetize treatment differences at both the individual trial and network levels and generate new knowledge for farmers and agronomists. The friendly interface enables users to explore trial summaries, access model outputs, and perform economic analysis at their fingertips. This paper describes a case-study to illustrate how to use the tool and make agronomic management decisions based on the on-farm trial data. We also provided technical details and guidance for developing a similar interactive visualization tool customized for on-farm research network.

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Interactive dynamics for visual analysis

Abstract: A taxonomy of tools that support the fluent and flexible use of visualizations.

Cited by 314 publications

References 22 publications

Structuring visual exploratory analysis of skill demand

Structuring visual exploratory analysis of skill demand

Development and evaluation of a three-dimensional multi-level model for visualising the workflow composite score in a health IT benchmark

Interactive Web‐based Data Visualization and Analysis Tool for Synthetizing on‐farm Research Networks Data

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