Towards Glyph-based visualizations for big data clustering

Keck, Mandy; Kammer, Dietrich; Gründer, Thomas; Thom, Thomas; Kleinsteuber, Martin; Maasch, Alexander; Groh, Rainer

doi:10.1145/3105971.3105979

Cited by 12 publications

(10 citation statements)

References 19 publications

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“…There are a number of statistical algorithms which can achieve this including Principal Component Analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE) and diffusion maps (Agrawal et al 2015;Fernandez et al 2015;Genender-Feltheimer 2018;Gisbrecht and Hammer 2015;Shirota et al 2017). Mapping multidimensional datasets into clusters that are represented in two or three dimensions is also common and allows the partitioning of data into similar groups (Keck et al 2017). It is useful to maintain the original structures of the data (Fernandez et al 2015) so that further analysis can be carried out after identifying certain clusters or patterns of interest during the two-or three-dimensional exploration of the data (Keck et al 2017;Xie et al 2016).…”

Section: Dimensionality Reductionmentioning

confidence: 99%

“…Mapping multidimensional datasets into clusters that are represented in two or three dimensions is also common and allows the partitioning of data into similar groups (Keck et al 2017). It is useful to maintain the original structures of the data (Fernandez et al 2015) so that further analysis can be carried out after identifying certain clusters or patterns of interest during the two-or three-dimensional exploration of the data (Keck et al 2017;Xie et al 2016). Advantages of dimensional reduction include easily understood visualisations, reduced data quality challenges, and improved computational efficiency (Genender-Feltheimer 2018).…”

Section: Dimensionality Reductionmentioning

confidence: 99%

“…An inevitable disadvantage of data reduction is information loss (Abidi et al 2018) resulting in the inability to identify nuances such as outliers as well as the true patterns and trends in the data (Keck et al 2017).…”

Section: Data Reductionmentioning

confidence: 99%

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Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review

Lowe

Matthee

2020

Lecture Notes in Computer Science

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The continual growth of big data necessitates efficient ways of analysing these large datasets. Data visualisation and visual analytics has been identified as a key tool in big data analysis because they draw on the human visual and cognitive capabilities to analyse data quickly, intuitively and interactively. However, current visualisation tools and visual analytical systems fall short of providing a seamless user experience and several improvements could be made to current commercially available visualisation tools. By conducting a systematic literature review, requirements of visualisation tools were identified and categorised into six groups: dimensionality reduction, data reduction, scalability and readability, interactivity, fast retrieval of results, and user assistance. The most common themes found in the literature were dimensionality reduction and interactive data exploration.

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Section: Dimensionality Reductionmentioning

confidence: 99%

Section: Dimensionality Reductionmentioning

confidence: 99%

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Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review

Lowe

Matthee

2020

Lecture Notes in Computer Science

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“…MetricsVis was designed with this key consideration in mind. In addition, LineUp utilizes bar charts to facilitate ranking comparison; MetricsVis employs radial layouts, which have outperformed tabular layouts when comparing data attributes [27] and provide compact visualization.…”

Section: Visual Analytics For Multi-attribute Decision Makingmentioning

confidence: 99%

“…10) into the design of the dandelion glyph. Inspired by previous research indicating that star plots with radial layout outperform tabular displays for comparing attribute values [27], we also adopted the radial layout into our dandelion glyph. In our dandelion glyph, the axes encode different attributes (categorical data) and the length of the axes encode the attribute values (numerical data).…”

Section: Dandelion Glyphmentioning

confidence: 99%

MetricsVis: A Visual Analytics System for Evaluating Employee Performance in Public Safety Agencies

Zhao

Karimzadeh

Snyder

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. MetricsVis overview: The priority adjustment view (2) encodes the crowdsourced crime severity ratings from police officers and citizens (perceived importance of factors); the red dots indicate the currently assigned weights used in the evaluation metrics. The projection view (6) shows the dimensionality reduction results. The group performance view (5) contains three visual representations that show an overview of group performance and the contribution of each member. The performance matrix view (3) displays the individual employee performance with employees in columns and job types in rows (here, employees are sorted based on their group first and then their total performance scores). The control panel shows the filters (1) and grouping method (4) applied in use case 1.Abstract-Evaluating employee performance in organizations with varying workloads and tasks is challenging. Specifically, it is important to understand how quantitative measurements of employee achievements relate to supervisor expectations, what the main drivers of good performance are, and how to combine these complex and flexible performance evaluation metrics into an accurate portrayal of organizational performance in order to identify shortcomings and improve overall productivity. To facilitate this process, we summarize common organizational performance analyses into four visual exploration task categories. Additionally, we develop MetricsVis, a visual analytics system composed of multiple coordinated views to support the dynamic evaluation and comparison of individual, team, and organizational performance in public safety organizations. MetricsVis provides four primary visual components to expedite performance evaluation: (1) a priority adjustment view to support direct manipulation on evaluation metrics; (2) a reorderable performance matrix to demonstrate the details of individual employees; (3) a group performance view that highlights aggregate performance and individual contributions for each group; and (4) a projection view illustrating employees with similar specialties to facilitate shift assignments and training. We demonstrate the usability of our framework with two case studies from medium-sized law enforcement agencies and highlight its broader applicability to other domains.

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Visual analysis of fitness landscapes in architectural design optimization

Abdelaal,

Galuschka,

Zorn

et al. 2024

Vis Comput

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In architectural design optimization, fitness landscapes are used to visualize design space parameters in relation to one or more objective functions for which they are being optimized. In our design study with domain experts, we developed a visual analytics framework for exploring and analyzing fitness landscapes spanning data, projection, and visualization layers. Within the data layer, we employ two surrogate models and three sampling strategies to efficiently generate a wide array of landscapes. On the projection layer, we use star coordinates and UMAP as two alternative methods for obtaining a 2D embedding of the design space. Our interactive user interface can visualize fitness landscapes as a continuous density map or a discrete glyph-based map. We investigate the influence of surrogate models and sampling strategies on the resulting fitness landscapes in a parameter study. Additionally, we present findings from a user study (N = 12), revealing how experts’ preferences regarding projection methods and visual representations may be influenced by their level of expertise, characteristics of the techniques, and the specific task at hand. Furthermore, we demonstrate the usability and usefulness of our framework by a case study from the architecture domain, involving one domain expert.

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Towards Glyph-based visualizations for big data clustering

Cited by 12 publications

References 19 publications

Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review

Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review

MetricsVis: A Visual Analytics System for Evaluating Employee Performance in Public Safety Agencies

Visual analysis of fitness landscapes in architectural design optimization

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