Cube Visualization Research Articles

Lexcube: Interactive Visualization of Large Earth System Data Cubes.

Many subsystems of Earth are constantly monitored in space and time and undergo continuous anthropogenic interventions. However, research into this transformation remains largely inaccessible to the public due to the complexity of the Big Data generated by models and Earth observation. To overcome this barrier, we present the Leipzig Explorer of Earth Data Cubes (lexcube.org), an interactive Earth data visualization that allows users to explore terabyte-scale datasets with minimal latency through space, time, variables, and model variants. With over 2800 users and 163,000 API requests since its public release in May 2022, lexcube.org is a novel interactive data cube visualization that embraces the concept of "data cubes," enabling an equal treatment of space and time. We expect this development to be particularly relevant for the emerging exascale Digital Twins of Earth, as interactive visualizations in real-time could remove access barriers and help democratize Earth system sciences.

Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real‐World Environments

AbstractThe distribution of visual attention can be evaluated using eye tracking, providing valuable insights into usability issues and interaction patterns. However, when used in real, augmented, and collaborative environments, new challenges arise that go beyond desktop scenarios and purely virtual environments. Toward addressing these challenges, we present a visualization technique that provides complementary views on the movement and eye tracking data recorded from multiple people in real‐world environments. Our method is based on a space‐time cube visualization and a linked 3D replay of recorded data. We showcase our approach with an experiment that examines how people investigate an artwork collection. The visualization provides insights into how people moved and inspected individual pictures in their spatial context over time. In contrast to existing methods, this analysis is possible for multiple participants without extensive annotation of areas of interest. Our technique was evaluated with a think‐aloud experiment to investigate analysis strategies and an interview with domain experts to examine the applicability in other research fields.

Immersive and interactive visualization of 3D spatio-temporal data using a space time hypercube: Application to cell division and morphogenesis analysis.

The analysis of multidimensional time-varying datasets faces challenges, notably regarding the representation of the data and the visualization of temporal variations. We propose an extension of the well-known Space-Time Cube (STC) visualization technique in order to visualize time-varying 3D spatial data, taking advantage of the interaction capabilities of Virtual Reality (VR). First, we propose the Space-Time Hypercube (STH) as an abstraction for 3D temporal data, extended from the STC concept. Second, through the example of embryo development imaging dataset, we detail the construction and visualization of a STC based on a user-driven projection of the spatial and temporal information. This projection yields a 3D STC visualization, which can also encode additional numerical and categorical data. Additionally, we propose a set of tools allowing the user to filter and manipulate the 3D STC which benefits the visualization, exploration and interaction possibilities offered by VR. Finally, we evaluated the proposed visualization method in the context of 3D temporal cell imaging data analysis, through a user study (n = 5) reporting the feedback from five biologists. These domain experts also accompanied the application design as consultants, providing insights on how the STC visualization could be used for the exploration of complex 3D temporal morphogenesis data.

HiSS-Cube: A scalable framework for Hierarchical Semi-Sparse Cubes preserving uncertainties

A wide variety of approaches are available for big data cube visualization and analysis. However, few exploit the power of array databases and none preserve the scientific uncertainties in measurements when constructing lower resolutions. In machine learning applications, we often need to rapidly search data for regions of interest and then focus on these areas, but without having to retrain the model every time we change the resolution. However, the reliable verification of these areas also requires details of the accuracy of the measured values. In this study, we developed a new software infrastructure called Hierarchical Semi-Sparse Cube (HiSS-Cube) based on Hierarchical Data Format version 5. HiSS-Cube enables visualization and machine learning using combined heterogeneous data and it was designed to be scalable for big data. HiSS-Cube allows data from multiple domains (imaging, spectral, and time-series data) to be combined and the construction of a multi-resolution semi-sparse data cube that preserves the uncertainties of scientific measurement at all resolutions. The functionality of HiSS-Cube was verified based on a subset of the Sloan Digital Sky Survey Stripe 82 survey. We compared the times and volumes for visualizations and machine learning data exported to HiSS-Cube and the original format (FITS). Using these data, we demonstrated that HiSS-Cube is faster by several orders of magnitude. HiSS-Cube supports export to the VOTable format and it is compatible with common Virtual Observatory tools. The source code for our prototype HiSS-Cube is available from GitHub and the data are available from Zenodo.

The Effect of Exploration Mode and Frame of Reference in Immersive Analytics.

The design space for user interfaces for Immersive Analytics applications is vast. Designers can combine navigation and manipulation to enable data exploration with ego- or exocentric views, have the user operate at different scales, or use different forms of navigation with varying levels of physical movement. This freedom results in a multitude of different viable approaches. Yet, there is no clear understanding of the advantages and disadvantages of each choice. Our goal is to investigate the affordances of several major design choices, to enable both application designers and users to make better decisions. In this article, we assess two main factors, exploration mode and frame of reference, consequently also varying visualization scale and physical movement demand. To isolate each factor, we implemented nine different conditions in a Space-Time Cube visualization use case and asked 36 participants to perform multiple tasks. We analyzed the results in terms of performance and qualitative measures and correlated them with participants' spatial abilities. While egocentric room-scale exploration significantly reduced mental workload, exocentric exploration improved performance in some tasks. Combining navigation and manipulation made tasks easier by reducing workload, temporal demand, and physical effort.

Development and application of an HDF5 schema for SKA-scale image cube visualization

In this paper, we describe an HDF5 schema created to support the efficient visualization of the large image cubes that will be produced by SKA Phase 1 and precursor radio telescopes. We demonstrate how the “HDF5-IDIA” schema’s features can improve the performance of visualization software, using both low-level metrics and real-world tests of the schema’s implementation in CARTA, an image viewer that is being developed to replace the existing CyberSKA and CASA viewers.

Bibliography, catalogs, pixel data: Management of heterogeneous Big Data at CDS by the documentalists

High speed Internet and the evolution of data storage space in terms of cost-effectiveness has changed the way data are managed today. Large amounts of heterogeneous data can now be visualized easily and rapidly using interactive applications such as “Google Maps”. In this respect, the Hierarchical Progressive Survey (HiPS) method has been developed by the Centre de Données astronomiques de Strasbourg (CDS) since 2009. HiPS uses the hierarchical sky tessellation called HEALPix to describe and organize images, data cubes or source catalogs. These HiPS can be accessed and visualized using applications such as Aladin. We show that structuring the data using HiPS enables easy and quick access to large and complex sets of astronomical data. As with bibliographic and catalog data, full documentation and comprehensive metadata are absolutely required for pertinent usage of these data. Hence the role of documentalists in the process of producing HiPS is essential. We present the interaction between documentalists and other specialists who are all part of the CDS team and support this process. More precisely, we describe the tools used by the documentalists to generate HiPS or to update the Virtual Observatory standardized descriptive information (the “metadata”). We also present the challenges faced by the documentalists processing such heterogeneous data on the scales of megabytes up to petabytes. On one hand, documentalists at CDS manage small size textual or numerical data for one or few astronomical objects. On the other hand, they process large data sets such as big catalogs containing heterogeneous data like spectra, images or data cubes, for millions of astronomical objects. Finally, by participating in the development of an interactive visualization of images or three-dimensional data cubes using the HiPS method, documentalists contribute to a long-term management of complex, large astronomical data.

Hashedcubes: Simple, Low Memory, Real-Time Visual Exploration of Big Data.

We propose Hashedcubes, a data structure that enables real-time visual exploration of large datasets that improves the state of the art by virtue of its low memory requirements, low query latencies, and implementation simplicity. In some instances, Hashedcubes notably requires two orders of magnitude less space than recent data cube visualization proposals. In this paper, we describe the algorithms to build and query Hashedcubes, and how it can drive well-known interactive visualizations such as binned scatterplots, linked histograms and heatmaps. We report memory usage, build time and query latencies for a variety of synthetic and real-world datasets, and find that although sometimes Hashedcubes offers slightly slower querying times to the state of the art, the typical query is answered fast enough to easily sustain a interaction. In datasets with hundreds of millions of elements, only about 2% of the queries take longer than 40ms. Finally, we discuss the limitations of data structure, potential spacetime tradeoffs, and future research directions.

Looking Into Saliency Model via Space-Time Visualization

We introduce a visual analytics method to analyze eye-tracking data and saliency models for dynamic stimuli, such as video or animated graphics. The focus lies on the analysis of the different performance of saliency models in contrast to human observers to identify trends in the general viewing behavior, including time sequences of attentional synchrony and objects with a strong attentional focus. By using a space-time cube visualization in combination with clustering, the dynamic stimuli and associated eye gazes as well as the attention maps from saliency models can be analyzed in a static three-dimensional representation. We propose algorithms to keep the appearance of the computer's attention data in line with the human's eye-tracking data. The analytical process is supported by multiple coordinated views that allow the user to focus on different aspects of spatial and temporal information in eye gaze data and saliency map. By comparing attention data from both human and computer incorporated with the spatiotemporal characteristics, we are able to find the different patterns within human and computer algorithms. We list our key findings to help developing better saliency detection algorithms.

An OLAM Operator for Multi-Dimensional Shrink

Shrink is an OLAM (On-Line Analytical Mining) operator based on hierarchical clustering, and it has been previously proposed in mono-dimensional form to balance precision with size in the visualization of cubes via pivot tables during OLAP analyses. It can be applied to the cube resulting from a query to decrease its size while controlling the approximation introduced; the idea is to fuse similar facts together and replace them with a single representative fact, respecting the bounds posed by dimension hierarchies. In this paper the authors propose a multi-dimensional generalization of the shrink operator, where facts are fused along multiple dimensions. Multi-dimensional shrink comes in two flavors: lazy and eager, where the bounds posed by hierarchies are respectively weaker and stricter. Greedy algorithms based on agglomerative clustering are presented for both lazy and eager shrink, and experimentally evaluated in terms of efficiency and effectiveness.

Shrink: An OLAP operation for balancing precision and size of pivot tables

Information flooding may occur during an OLAP session when the user drills down her cube up to a very fine-grained level, because the huge number of facts returned makes it very hard to analyze them using a pivot table. To overcome this problem we propose a novel OLAP operation, called shrink, aimed at balancing data precision with data size in cube visualization via pivot tables. The shrink operation fuses slices of similar data and replaces them with a single representative slice, respecting the constraints suggested by dimension hierarchies, until the result has either size or error smaller than a given threshold. An optimal computation of the shrink operation has exponential complexity, so we present both a greedy algorithm based on agglomerative clustering, which returns a sub-optimal solution, and a branch-and-bound algorithm that returns an optimal solution. Finally, we discuss some experimental results to evaluate the shrink operation from the efficiency and effectiveness point of view.

Space-Time Visual Analytics of Eye-Tracking Data for Dynamic Stimuli

We introduce a visual analytics method to analyze eye movement data recorded for dynamic stimuli such as video or animated graphics. The focus lies on the analysis of data of several viewers to identify trends in the general viewing behavior, including time sequences of attentional synchrony and objects with strong attentional focus. By using a space-time cube visualization in combination with clustering, the dynamic stimuli and associated eye gazes can be analyzed in a static 3D representation. Shotbased, spatiotemporal clustering of the data generates potential areas of interest that can be filtered interactively. We also facilitate data drill-down: the gaze points are shown with density-based color mapping and individual scan paths as lines in the space-time cube. The analytical process is supported by multiple coordinated views that allow the user to focus on different aspects of spatial and temporal information in eye gaze data. Common eye-tracking visualization techniques are extended to incorporate the spatiotemporal characteristics of the data. For example, heat maps are extended to motion-compensated heat maps and trajectories of scan paths are included in the space-time visualization. Our visual analytics approach is assessed in a qualitative users study with expert users, which showed the usefulness of the approach and uncovered that the experts applied different analysis strategies supported by the system.

ABSTRACT: The Use of Coherence Cube Visualization in Picking Seismic Processing Parameters and Post Processing Hybrid Visualization Products

Abstract This talk details the use of Coherence cube visualization as an additional Q.C. tool to define processing parameters. In addition, combining other attributes in the coherence measurement can form custom visualization cubes. Traditionally, processing analysts Q.C. their product by displaying inlines and crosslines from a 3D volume either on paper or interactively on their computer screen. The third dimension in a 3D volume is an amplitude slice. The amplitude slice, while offering a different view of our seismic data, does very little to help us understand processing improvements or see depositional environments. Migrating and producing a coherence cube on each step within the processing sequence has given us a way to view a third dimension of our seismic data as a coherence time slice or a horizon slice. By viewing the data in this fashion we are able to see subtle differences in processing parameters which are not apparent by viewing inlines and crosslines alone. Some of the processes are: geometry, static's, dmo, migration velocity evaluation, migration algorithm evaluation and signal to noise evaluation. The final result of this evaluation is normally a pre-stack time migrated seismic volume and a final coherence volume. Depending on the geology, we are able to combine other seismic attributes with coherence measurements to directly define hydrocarbons or define facies changes such as the boundary between sand and shale. The paper details the use of coherence cube visualization as an additional quality control tool to define processing parameters. The added resolution that coherence cube gives us in the time slice and horizon slice domain allows us to see subtle differences in processing parameters which are not always readily apparent while looking at conventional displays of our seismic inlines and crosslines. The detailed examples will show the benefits and power of using coherence cubes to visualize the data during each step of the processing sequence.