Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations.
Abstract not provided.
Matzen, Laura E.↗
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Systems and methods of generating a plurality of data visualizations in a data visualization region
An exemplary method of generating a plurality of data visualizations comprises displaying a graphical user interface that includes a schema information region and a data visualization region. The method further includes receiving user actions to associate first, second, and third fields included in the schema information region with a plurality of shelves of the data visualization region. The method further includes generating a plurality of data visualizations in the data visualization region in accordance with the received user actions, each data visualization includes a plurality of data marks corresponding to a respective retrieved tuple of data having the first, second, and third fields. Moreover, each of the data marks has an x-position and y-position defined according to data for the first and second fields, respectively. In addition, each of the data visualizations corresponds to a respective unique value of the third field, and is displayed in a respective pane.
97 MATHEMATICS AND COMPUTING↗
Interactive Data Visualization
Interactive data visualization leverages human visual perception and cognition to improve the accuracy and effectiveness of data analysis. When combined with automated data analytics, data visualization systems orchestrate the strengths of humans with the computational power of machines to solve problems neither approach can manage in isolation. In the intelligent transportation system domain, such systems are necessary to support decision making in large and complex data streams. In this chapter, we provide an introduction to several key topics related to the design of data visualization systems. In addition to an overview of key techniques and strategies, we will describe practical design principles. The chapter is concluded with a detailed case study involving the design of a multivariate visualization tool.
information visualization↗
Web-based visual data exploration for improved radiological source detection
Radiation detection can provide a reliable means of detecting radiological material. Such capabilities can help to prevent nuclear and/or radiological attacks, but reliable detection in uncontrolled surroundings requires algorithms that account for environmental background radiation. The Berkeley Data Cloud (BDC) facilitates the development of such methods by providing a framework to capture, store, analyze, and share data sets. In the era of big data, both the size and variety of data make it difficult to explore and find data sets of interest and manage the data. Thus, in the context of big data, visualization is critical for checking data consistency and validity, identifying gaps in data coverage, searching for data relevant to an analyst's use cases, and choosing input parameters for analysis. Downloading the data and exploring it on an analyst's desktop using traditional tools are no longer feasible due to the size of the data. This study describes the design and implementation of a visualization system that addresses the problems associated with data exploration within the context of the BDC. Finally, the visualization system is based on a JavaScript front end communicating via REST with a back end web server.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Xi-cam : a versatile interface for data visualization and analysis
Xi-cam is an extensible platform for data management, analysis and visualization. Xi-cam aims to provide a flexible and extensible approach to synchrotron data treatment as a solution to rising demands for high-volume/high-throughput processing pipelines. The core of Xi-cam is an extensible plugin-based graphical user interface platform which provides users with an interactive interface to processing algorithms. Plugins are available for SAXS/WAXS/GISAXS/GIWAXS, tomography and NEXAFS data. With Xi-cam 's `advanced' mode, data processing steps are designed as a graph-based workflow, which can be executed live, locally or remotely. Remote execution utilizes high-performance computing or de-localized resources, allowing for the effective reduction of high-throughput data. Xi-cam 's plugin-based architecture targets cross-facility and cross-technique collaborative development, in support of multi-modal analysis. Xi-cam is open-source and cross-platform, and available for download on GitHub.
Pandolfi, Ronald J. (ORCID:0000000308248548)↗
Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations Supplemental Materials.
Abstract not provided.
Matzen, Laura E.↗
Real Time Streaming Data visualization tooling (RTS Data Vis App)
NREL's RTS Data Vis App contains the following sub-projects: (1) Conditioner: A tool to resample high-rate data from the relay for low-rate visualization and (2) Chart: A tool to plot the data from the relay, including a specialized copy of the Conditioner. The Conditioner is a command-line tool for reading ADMS data from ZMQ, resampling, and exporting over a websocket. The Chart is an example of a charting tool for ADMS applications, using a ZMQ message bus.
Wunder, Nicholas↗
NeuralCubes: Deep Representations for Visual Data Exploration
Visual exploration of large multi-dimensional datasets has seen tremendous progress in recent years, allowing users to express rich data queries that produce informative visual summaries, all in real time. Techniques based on data cubes are some of the most promising approaches. However, these techniques usually require a large memory footprint for large datasets. To tackle this problem, we present NeuralCubes: neural networks that predict results for aggregate queries, similar to data cubes. NeuralCubes learns a function that takes as input a given query, for instance, a geographic region and temporal interval, and outputs the result of the query. The learned function serves as a real-time, low-memory approximator for aggregation queries. Our models are small enough to be sent to the client side (e.g. the web browser for a web-based application) for evaluation, enabling data exploration of large datasets without database/network connection. Here, we demonstrate the effectiveness of NeuralCubes through extensive experiments on a variety of datasets and discuss how NeuralCubes opens up opportunities for new types of visualization and interaction.
97 MATHEMATICS AND COMPUTING↗
Data Visualization Saliency v. 1.0
Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off human subject studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. The visual saliency model for data visualizations described here addresses this need for evaluation tools. Visual saliency models assess the visual features (e.g. color, contrast, edges) of an image to predict which areas of that image will draw a viewer’s attention. Providing predictions of where viewers will look in a visualization based on the saliency of the various regions and elements in that image could help designers to assess whether or not their design will draw the viewer’s attention as intended. The Data Visualization Saliency model, a saliency model for data visualizations, consists of two parts: a text saliency map and a modified version of the Itti, Koch and Niebur model [1]. The final saliency map is a linear combination of the two. The text saliency map is created from an algorithm that computes the likelihood of belonging to a text region for each pixel of an input visualization image. The modified Itti model transforms the original input image to a representation in CIE LAB color space, which provides an accurate representation of perceptual color opponency. For a given input data visualization image, the Data Visualization Saliency model computes and returns the visualization saliency map highlighting areas of the data visualization that will draw a viewer’s attention. It also returns the lower level saliency maps for text and the modified Itti model.
Haass, Michael↗
Systems and methods of generating data marks in data visualizations
An example method of displaying a data visualization includes displaying a plurality of selectable fields and receiving user selections of two different fields from the plurality of selectable fields. The method also includes generating, in accordance with the received user selections, data marks to be displayed in a data visualization, each data mark corresponding to a respective retrieved tuple of data from a multidimensional database, where (i) each data mark has an x-position defined according to data for a first field in the respective tuple and (ii) each data mark has a y-position defined according to data for a second field in the respective tuple. The method also includes displaying the data visualization that includes the generated data marks.
97 MATHEMATICS AND COMPUTING↗
Systems and methods of generating data marks in data visualizations
An example method of displaying a data visualization includes displaying a graphical user interface that includes three distinct regions, and receiving user actions to associate two different fields with two different shelves. The method also includes generating, in accordance with the received user actions, data marks to be displayed in a data visualization, each data mark corresponding to a respective retrieved tuple of data from a multidimensional database, where (i) each data mark has an x-position defined according to data for a first field in the respective tuple and (ii) each data mark has a y-position defined according to data for a second field in the respective tuple. The method also includes displaying a data visualization that includes the generated data marks.
Stolte, Chris↗
Systems and methods of generating color saturated data marks in data visualizations
A method of displaying data visualizations includes retrieving a dataset from a multidimensional database, where the dataset includes a plurality of fields, including first and second fields, and the first field is a data hierarchy that includes multiple subfields. The method further includes displaying a GUI with a first region displaying the plurality of fields (with the first field in a collapsed state), a second region with a plurality of shelves, and a third region. In response to receiving a user action to display the first field in an expanded state, the method displays the first field and the multiple subfields in a hierarchical structure. The method receives additional user actions to: (i) associate a first subfield of the plurality with a first shelf, and (ii) associate the second field with a second shelf, and then displays, in accordance with the user actions, a data visualization in the third region.
Stolte, Chris↗
TangibleData: Interactive Data Visualization with Mid-Air Haptics
In this paper, we investigate the effects of mid-air haptics in interactive 3D data visualization. We build an interactive 3D data visualization tool that adapts hand gestures and mid-air haptics to provide tangible interaction in VR using ultrasound haptic feedback on 3D data visualization. We consider two types of 3D visualization datasets and provide different data encoding methods for haptic representations. Two user experiments are conducted to evaluate the effectiveness of our approach. The first experimental results show that adding a mid-air haptic modality can be beneficial regardless of noise conditions and useful for handling occlusion or discerning density and volume information. The second experiment results further show the strengths and weaknesses of direct touch and indirect touch modes. Our findings can shed light on designing and implementing a tangible interaction on 3D data visualization with mid-air haptic feedback.
Bhardwaj, Ayush↗
Improving Scientific Data Analysis Through Multi-touch Enabled Interactive Data Visualization with Applications to Neutron Science
Scientific data analysis is an inherently exploratory process that requires flexible human interaction with data visualization and anal- ysis tools. In this paper, we describe challenges in neutron scattering data analysis and present our approach for allowing more effective human interaction. In addition to multi-touch enabled data visualiza- tion techniques, we describe color scale enhancements, layouts, and visual analytics approaches for 3D volume slicing. Since challenges in neutron science are echoed in most scientific fields, we generalize the project objective and present a case for bridging the gap between the latest advances in interactive data visualization and practical scientific scenarios where new critical knowledge is buried in large volumes of complex data.
Steed, Chad↗
SNF Data Visualization on the Centralized Used Fuel Resource for Information Exchange (CURIE) Website
To better inform stakeholders regarding the nature of spent nuclear fuel (SNF) at utility sites, an online data visualization system has been created for the U.S. Department of Energy's Office of Nuclear Energy (DOENE). The data visualization system is part of the Centralized Used Fuel Resource for Information Exchange (CURIE) website, hosted by the Oak Ridge National Laboratory (ORNL) for DOE-NE. The visualization employs a database that is part of an analysis tool known as the Used Nuclear Fuel Storage, Transportation and Disposal Analysis Resource and Data System (UNF-ST[and]DARDS). Because many of the previous document management and sharing tools related to SNF have been terminated (or are inaccessible), a new tool, the CURIE, was developed for DOE-NE and launched on March 1, 2013 at http://curie.ornl.gov. In addition to supporting DOE SNF programs, CURIE is a national resource that is accessible to industry, vendor, federal, and laboratory partners, as well as other stakeholders. CURIE's architecture was initially based on previous work that supported DOE's Bioenergy Technologies Office on the Bioenergy Knowledge Discovery Framework (KDF). The site uses an open-source search engine, Apache Solr, which provides intuitive search results based on a faceted search method similar to that used by Amazon.com{sup R}. This algorithm helps to ensure that finding documents and data is straightforward. SNF data visualization capability was added to the CURIE website which provides a capability to post and access documents and data related to the management of SNF. CURIE currently maintains over 4,100 documents and the Siting Experience Database (SED), which was developed in response to a near-term recommendation by the Blue Ribbon Commission on America's Nuclear Future (BRC). The SED is a database of the experience that has been gained and relevant documentation that has been produced in efforts to site nuclear waste facilities, both in the US and abroad. Most of these documents are publicly accessible. In addition, CURIE maintains a current calendar of related events, an SNF image gallery, featured documents, and external links to related databases and websites. CURIE also maintains private groups (or communities) to facilitate collaborative development through sharing and editing documents that are not publicly available. There are more than 250 registered users in 23 different communities within CURIE.ORNL has also been developing UNF-ST[and]DARDS to provide a controlled source of technical data for various waste management system analysis/evaluation tools, access to key technical data, and analysis capabilities for characterizing the SNF inventory. UNF-ST[and]DARDS also provides enhanced capabilities to assess safety, risk, and uncertainty throughout the integrated waste management system. This paper describes the UNF-ST[and]DARDS data available for visualization and the associated visualization techniques used by CURIE to display this data.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Data Visualization: Augmented Reality
In recent years, there has been an increasing interest in developing new technologies for automated characterization and visualization of condition monitoring data. Augmented Reality (AR) is a technology that is being developed to improve such data visualization. Augmented reality has been defined as a technology that merges virtual and physical components in real-time, and in three dimensions. Wearable, commercially-available AR devices allow onsite engineers and technicians to perform inspection tasks with significantly more available information such as comparisons of past and present sensor and imager data, onsite data analysis and result displays, and various forms of metadata including technical drawings, previous inspection reports and maintenance histories, operation manuals, codes and standards, and holograms representing data analysis results superimposed onto the in situ monitored system.
42 ENGINEERING↗
Visual Cognition: Human Comprehension of Data Visualizations.
Abstract not provided.
Haass, Michael Joseph↗