Progressive Splatting of Continuous Scatterplots and Parallel Coordinates

Heinrich, Julian; Bachthaler, Sven; Weiskopf, Daniel

doi:10.1111/j.1467-8659.2011.01914.x

Cited by 35 publications

(26 citation statements)

References 31 publications

(18 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our main idea was based on interpolation in attribute space, which is possible due to a meaningful neighborhood structure in physical space that can be imposed onto the attribute space. Similar observations have been used in the concept of continuous scatterplots [26][27][28][29][30]. Continuous scatterplots generalize the concept of scatterplots to the visualization of spatially continuous input data by a continuous and dense plot.…”

Section: Interpolation In Attribute Spacementioning

confidence: 87%

Upsampling for Improved Multidimensional Attribute Space Clustering of Multifield Data

Molchanov

Linsen

2018

Information

View full text Add to dashboard Cite

Clustering algorithms in the high-dimensional space require many data to perform reliably and robustly. For multivariate volume data, it is possible to interpolate between the data points in the high-dimensional attribute space based on their spatial relationship in the volumetric domain (or physical space). Thus, sufficiently high number of data points can be generated, overcoming the curse of dimensionality for this particular type of multidimensional data. We applies this idea to a histogram-based clustering algorithm. We created a uniform partition of the attribute space in multidimensional bins and computed a histogram indicating the number of data samples belonging to each bin. Without interpolation, the analysis was highly sensitive to the histogram cell sizes, yielding inaccurate clustering for improper choices: Large histogram cells result in no cluster separation, while clusters fall apart for small cells. Using an interpolation in physical space, we could refine the data by generating additional samples. The depth of the refinement scheme was chosen according to the local data point distribution in attribute space and the histogram's bin size. In the case of field discontinuities representing sharp material boundaries in the volume data, the interpolation can be adapted to locally make use of a nearest-neighbor interpolation scheme that avoids averaging values across the sharp boundary. Consequently, we could generate a density computation, where clusters stay connected even when using very small bin sizes. We exploited this result to create a robust hierarchical cluster tree, apply our technique to several datasets, and compare the cluster trees before and after interpolation.

show abstract

Section: Interpolation In Attribute Spacementioning

confidence: 87%

Upsampling for Improved Multidimensional Attribute Space Clustering of Multifield Data

Molchanov

Linsen

2018

Information

View full text Add to dashboard Cite

show abstract

“…This is by no means a simple task and adapting existing techniques to the PVA concept is a complex research question in its own regard, very similar to the problem of parallelizing existing algorithms for their distributed use. For some techniques, progressive variants have already been proposed -for example, for k-Means [52], MDS [53], t-SNE [54], Treemaps [55], and Parallel Coordinates [56,57]. Yet these are merely a tiny fraction of the analysis and visualization techniques our community are accustomed to use.…”

Section: Discussionmentioning

confidence: 99%

A Review and Characterization of Progressive Visual Analytics

et al. 2018

View full text Add to dashboard Cite

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA's principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions.Keywords: visual analytics; progressive visualization; incremental visualization; online algorithms MotivationWith data growing in size and complexity, and analysis methods getting more sophisticated and computationally intensive, the idea of Progressive Visual Analytics (PVA) [1,2] becomes increasingly appealing. A PVA approach can either subdivide the data to process each data chunk individually, or it can subdivide the analytic process into computational steps that iteratively refine analytic results [3]. By doing so, PVA yields partial results of increasing completeness or approximative results of increasing correctness, respectively. This is useful in a wide range of visual analytics scenarios:• to realize responsive client-server visualizations using incremental data transmissions Because of its versatility, the progressive approach to data analysis and visualization is alternatively seen as a paradigm for computation, for interaction, for data transmission, or for visual presentation. It is thus not surprising that PVA-related research is distributed over multiple disciplines, motivated by various underlying problems, described in different, sometimes overloaded terms at different levels of detail for different audiences.

show abstract

“…This problem is addressed by Bachthaler and Weiskopf [BW08] providing an analytical solution to the computation of the corresponding density in scatterplots, which can also be applied to parallel coordinates [HW09]. The continuous model allows for the extraction of critical points [LT10], other features [LT11], the design of transfer functions [GXY11], [WZL*12] and can be computed efficiently [BW09], [HBW11]. Recently, Molchanov et al .…”

Section: Related Workmentioning

confidence: 99%

Visual Analysis of Trajectories in Multi‐Dimensional State Spaces

Grottel

Heinrich

Weiskopf

et al. 2014

Computer Graphics Forum

Self Cite

View full text Add to dashboard Cite

Multi-dimensional data originate from many different sources and are relevant for many applications. One specific sub-type of such data is continuous trajectory data in multi-dimensional state spaces of complex systems. We adapt the concept of spatially continuous scatterplots and spatially continuous parallel coordinate plots to such trajectory data, leading to continuous-time scatterplots and continuous-time parallel coordinates. Together with a temporal heat map representation, we design coordinated views for visual analysis and interactive exploration. We demonstrate the usefulness of our visualization approach for three case studies that cover examples of complex dynamic systems: cyber-physical systems consisting of heterogeneous sensors and actuators networks (the collection of time-dependent sensor network data of an exemplary smart home environment), the dynamics of robot arm movement and motion characteristics of humanoids.

show abstract

Progressive Splatting of Continuous Scatterplots and Parallel Coordinates

Cited by 35 publications

References 31 publications

Upsampling for Improved Multidimensional Attribute Space Clustering of Multifield Data

Upsampling for Improved Multidimensional Attribute Space Clustering of Multifield Data

A Review and Characterization of Progressive Visual Analytics

Visual Analysis of Trajectories in Multi‐Dimensional State Spaces

Contact Info

Product

Resources

About