Towards Quantitative Visual Analytics with Structured Brushing and Linked Statistics

Rados, Sanjin; Splechtna, Rainer; Matković, Krešimir; źuras, M.; Gröller, Eduard; Hauser, Helwig

doi:10.1111/cgf.12901

Cited by 24 publications

(14 citation statements)

References 21 publications

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“…Features supported by visual analytics environments such as brushing and linking can be used for hypothesis generation and testing for time‐varying climate datasets [KLM∗08, RSM∗16]. Trends identified during analysis can be used to select regions that can act as climate change indicators.…”

Section: Taxonomiesmentioning

confidence: 99%

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

Afzal

Hittawe

Ghani

et al. 2019

Computer Graphics Forum

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The analysis of ocean and atmospheric datasets offers a unique set of challenges to scientists working in different application areas. These challenges include dealing with extremely large volumes of multidimensional data, supporting interactive visual analysis, ensembles exploration and visualization, exploring model sensitivities to inputs, mesoscale ocean features analysis, predictive analytics, heterogeneity and complexity of observational data, representing uncertainty, and many more. Researchers across disciplines collaborate to address such challenges, which led to significant research and development advances in ocean and atmospheric sciences, and also in several relevant areas such as visualization and visual analytics, big data analytics, machine learning and statistics. In this report, we perform an extensive survey of research advances in the visual analysis of ocean and atmospheric datasets. First, we survey the task requirements by conducting interviews with researchers, domain experts, and end users working with these datasets on a spectrum of analytics problems in the domain of ocean and atmospheric sciences. We then discuss existing models and frameworks related to data analysis, sense‐making, and knowledge discovery for visual analytics applications. We categorize the techniques, systems, and tools presented in the literature based on the taxonomies of task requirements, interaction methods, visualization techniques, machine learning and statistical methods, evaluation methods, data types, data dimensions and size, spatial scale and application areas. We then evaluate the task requirements identified based on our interviews with domain experts in the context of categorized research based on our taxonomies, and existing models and frameworks of visual analytics to determine the extent to which they fulfill these task requirements, and identify the gaps in current research. In the last part of this report, we summarize the trends, challenges, and opportunities for future research in this area. (see http://www.acm.org/about/class/class/2012)

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Section: Taxonomiesmentioning

confidence: 99%

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

Afzal

Hittawe

Ghani

et al. 2019

Computer Graphics Forum

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“…Recently, the Mahalanobis brush was presented as an interesting alternative for brushing scatterplots [RSM*16]. The user simply clicks into the center of a coherent data subset to be selected.…”

Section: Related Workmentioning

confidence: 99%

“… brushing using simple geometries —the most commonly used brushing solutions include the rectangular or circular brushing on scatterplots, line‐brushing on data graphs [KMG*06], etc. lassoing —the user selects subsets by drawing a geometrically detailed lasso around the target group of item representations logical combinations of simple brushes —the user makes use of multiple brushes and combines them using logical operators to refine the data selection [MW95, DGH03] sketch‐based brushing —the user sketches a shape onto a visualization and a selection heuristic is used to determine which data are selected [MKO*08, FH17, RSM*16] …”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate CNN‐based Brushing in Scatterplots

Fan

Hauser

2018

Computer Graphics Forum

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Brushing plays a central role in most modern visual analytics solutions and effective and efficient techniques for data selection are key to establishing a successful human-computer dialogue. With this paper, we address the need for brushing techniques that are both fast, enabling a fluid interaction in visual data exploration and analysis, and also accurate, i.e., enabling the user to effectively select specific data subsets, even when their geometric delimination is non-trivial. We present a new solution for a near-perfect sketch-based brushing technique, where we exploit a convolutional neural network (CNN) for estimating the intended data selection from a fast and simple click-and-drag interaction and from the data distribution in the visualization.Our key contributions include a drastically reduced error rate-now below 3%, i.e., less than half of the so far best accuracyand an extension to a larger variety of selected data subsets, going beyond previous limitations due to linear estimation models.

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“…The keyframed brushing mechanism is intended to reshape (a certain subset of) analytical tasks as a dialogue while keeping the user engaged. This methodology has been shown to generate dynamic visual summaries [44] and structured selection sequences [29] and we employ this technique here. The user defines two or more brushes (according to his/her analytical goal), similar to defining key frames in computer-assisted animation [10].…”

Section: Keyframed Brushingmentioning

confidence: 99%

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

Turkay

Kaya

Balcısoy

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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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.

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Towards Quantitative Visual Analytics with Structured Brushing and Linked Statistics

Cited by 24 publications

References 21 publications

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

Fast and Accurate CNN‐based Brushing in Scatterplots

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

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