Uncertainty-Aware Multidimensional Ensemble Data Visualization and Exploration

Chen, Haidong; Zhang, Song; Chen, Wei; Mei, Honghui; Zhang, Jiawei; Mercer, Andrew E.; Liang, Ronghua; Qu, Huamin

doi:10.1109/tvcg.2015.2410278

Cited by 71 publications

(36 citation statements)

References 54 publications

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“…Multidimensional data is commonly visualized on 2D displays with either data being summarized with fewer dimensions (please refer to section 6.4 for more details) [FBW16, KFH10, SWMW09], using multiple views for different attributes/dimensions [LTJ10, BBP08, LLEJ11], or with parallel coordinates [WLSL17, CZC∗15]. Parallel coordinates is a commonly used visualization technique for multidimensional data (Figure 1 (A) and Figure 4).…”

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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“…Anomaly detection has been extensively studied over the past years [12]. Methods for anomaly detection can be broadly categorized into tensorbased algorithms [13], statistics-based algorithms [41], classificationbased algorithms [33], and neighbor-based or distance-based algorithms [6]. Although these methods are effective in identifying anomalies with numeric results, they are not capable of considering the sequential structure when detecting anomalies for event sequences.…”

Section: Anomaly Detection Algorithmsmentioning

confidence: 99%

Visual Anomaly Detection in Event Sequence Data

Guo

Jin

Chen

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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Fig. 1. The anomaly detection result of the Agavue dataset using ET 3 system. Two anomalous pattern were identified: (1) tool-tips failing to show when users interact with toolbars; (2) users encountering an error after requesting data binding.Abstract-Anomaly detection is a common analytical task that aims to identify rare cases that differ from the typical cases that make up the majority of a dataset. Anomaly detection takes many forms, but is frequently applied to the analysis of event sequence data in addressing real-world problems such as error diagnosis, fraud detection, and vital sign monitoring. With general event sequence data, however, the task of anomaly detection can be complex because the sequential and temporal nature of such data results in diverse definitions and flexible forms of anomalies: a specific event type, a sub-sequence or the entire sequence. This, in turn, increases the difficulty in interpreting detected anomalies, a critical element in raising human confidence with the analysis results. Most prior work in event sequence anomaly detection focuses on detecting only one type of anomaly with a specific application domain. Such approaches do not provide flexible ways of identifying diverse types of anomalies, and provide only limited support for result interpretation. In this paper, we propose an unsupervised anomaly detection algorithm based on Variational AutoEncoders (VAE). The model learns latent representations for all sequences in the dataset and detects anomalies that deviate from the overall distribution. Moreover, the model can estimate an underlying normal progression for each given sequence represented as occurrence probabilities of events along the sequence progression. Events in violation of their occurrence probability (i.e., event occurrences with small occurrence probability, and absent events with large occurrence probability) are identified as abnormal. We also introduce a visualization system, EventThread3, to support interactive exploration of the analysis result. The system facilitates interpretations of anomalies within the context of normal sequence progressions in the dataset through comprehensive one-to-many sequence comparison. Finally, we quantitatively evaluate the performance of our anomaly detection algorithm and demonstrate the effectiveness of our system through case studies in three different application domains and report feedback collected from study participants and expert users.

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“…An approach for uncertainty‐aware multi‐dimensional ensemble data visualization and exploration was recently presented by Chen et al . [CZC*15], but such approaches do not allow for comparing behaviour patterns of individual simulations over time. An approach for the analysis of ensemble data sets using statistical descriptors was developed by Potter et al .…”

Section: Background and Related Workmentioning

confidence: 99%

Projected Field Similarity for Comparative Visualization of Multi‐Run Multi‐Field Time‐Varying Spatial Data

Fofonov

Linsen

2018

Computer Graphics Forum

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The purpose of multi‐run simulations is often to capture the variability of the output with respect to different initial settings. Comparative analysis of multi‐run spatio‐temporal simulation data requires us to investigate the differences in the dynamics of the simulations' changes over time. To capture the changes and differences, aggregated statistical information may often be insufficient, and it is desirable to capture the local differences between spatial data fields at different times and between different runs. To calculate the pairwise similarity between data fields, we generalize the concept of isosurface similarity from individual surfaces to entire fields and propose efficient computation strategies. The described approach can be applied considering a single scalar field for all simulation runs or can be generalized to a similarity measure capturing all data fields of a multi‐field data set simultaneously. Given the field similarity, we use multi‐dimensional scaling approaches to visualize the similarity in two‐dimensional or three‐dimensional projected views as well as plotting one‐dimensional similarity projections over time. Each simulation run is depicted as a polyline within the similarity maps. The overall visual analysis concept can be applied using our proposed field similarity or any other existing measure for field similarity. We evaluate our measure in comparison to popular existing measures for different configurations and discuss their advantages and limitations. We apply them to generate similarity maps for real‐world data sets within the overall concept for comparative visualization of multi‐run spatio‐temporal data and discuss the results.

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Uncertainty-Aware Multidimensional Ensemble Data Visualization and Exploration

Cited by 71 publications

References 54 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

Visual Anomaly Detection in Event Sequence Data

Projected Field Similarity for Comparative Visualization of Multi‐Run Multi‐Field Time‐Varying Spatial Data

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