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Matches, Mismatches, and Methods: Multiple-view Workflows for Energy Portfolio analysis

IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2016年第1期22卷 449-458页

作者： Brehmer, Matthew Ng, Jocelyn Tate, Kevin Munzner, Tamara Univ British Columbia Vancouver BC V5Z 1M9 Canada EnerNOC Inc Boston MA USA

The energy performance of large building portfolios is challenging to analyze and monitor, as current analysis tools are not scalable or they present derived and aggregated data at too coarse of a level. We conducted a visualization design study, beginning with a thorough work domain analysis and a characterization of data and task abstractions. We describe generalizable visual encoding design choices for time-oriented data framed in terms of matches and mismatches, as well as considerations for workflow design. Our designs address several research questions pertaining to scalability, view coordination, and the inappropriateness of line charts for derived and aggregated data due to a combination of data semantics and domain convention. We also present guidelines relating to familiarity and trust, as well as methodological considerations for visualization design studies. Our designs were adopted by our collaborators and incorporated into the design of an energy analysis software application that will be deployed to tens of thousands of energy workers in their client base.

关键词： Design study design methodologies time series data task and requirements analysis coordinated and multiple views

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exvis: A visual analysis tool for wind tunnel data

exVis: A visual analysis tool for wind tunnel data

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conference on visual data exploration and analysis v

作者： Deardorff, DG Keeley, LE Uselton, SP NASA Ames Res Ctr MRJ Technol Solut Moffett Field CA 94035 USA

ISBN: (纸本)0819427381

exvis is a software tool created to support interactive display and analysis of data collected during wind tunnel experiments. It is a result of a continuing project to explore the uses of information technology in improving the effectiveness of aeronautical design professionals. The data analysis goals are accomplished by allowing aerodynamicists to display and query data collected by new data acquisition systems and to create traditional wind tunnel plots from this data by interactively interrogating these images. exvis was built as a collection of distinct modules to allow for rapid prototyping, to foster evolution of capabilities, and to facilitate object reuse within other applications being developed. It was implemented using C++ and Open Inventor, commercially available object-oriented tools. The initial version was composed of three main classes. Two of these modules are autonomous viewer objects intended to display the test images (Imageviewer) and the plots (Graphviewer). The third main class is the Application User Interface (AUI) which manages the passing of data and events between the viewers, as well as providing a user interface to certain features. User feedback was obtained on a regular basis, which allowed for quick revision cycles and appropriately enhanced feature sets. During the development process additional classes were added, including a color map editor and a data set manager. The Imageviewer module was substantially rewritten to add features and to use the data set manager. The use of an object-oriented design was successful in allowing rapid prototyping and easy feature addition.

关键词： experimental data interactive analysis comparative visualization aerodynamics image querying

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An Interactive visualization System for Near-Earth Space exploration data 4

An Interactive Visualization System for Near-Earth Space Exp...

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4th International conference on Environmental Science and Material Application (ESMA)

作者： Yu, Haijun Li, Shengyang Zhang, Tao Univ Chinese Acad Sci CAS Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Beijing Peoples R China

Aiming at the actual needs of efficient and convenient visual analysis of near-Earth space exploration data, this paper studies the functional realization mode, data flow and interactive method, analyzes the functional framework and data flow of the interactive visualization system of near-Earth space exploration data in detail, designs a multi-level, loosely coupled and easily extensible system architecture, and focuses on solving the logic model design, the fusion rendering of the multivariable data, the shadow calculation, the data clustering analysis, and other key technologies. The application of the system has realized the interactive and visual analysis of the massive and multi-source near-Earth space exploration data, and provided convenient data analysis service for the vast number of scientific research and application users, which helps to better play the potential value of near-Earth space exploration data.

关键词： Space research

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Big data exploration through visual Analytics

Big Data Exploration through Visual Analytics

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IEEE conference on visual Analytics Science and Technology (vAST)

作者： Abousalh-Neto, Nascif A. Kazgan, Sumeyye SAS Institute United States

ISBN: (纸本)9781467347532

SAS (R) visual Analytics Explorer is an advanced data visualization and exploratory data analysis application that is a component of the SAS visual Analytics solution. It excels at handling big data problems like the vAST challenge. With a wide range of visual analytics features and the ability to scale to massive datasets, SAS visual Analytics Explorer enables analysts to find patterns and relationships quickly and easily, no matter the size of their data. In this summary paper, we explain how we used SAS visual Analytics Explorer to solve the vAST Challenge 2012 mini-challenge 1.

关键词： visual analytics big data data visualization exploratory data analysis

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visualization of geographically-referenced observations to support real-time decision-making

Visualization of geographically-referenced observations to s...

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conference on visual data exploration and analysis v

作者： Mantey, PE Univ Calif Santa Cruz Jack Baskin Sch Engn Santa Cruz CA 95064 USA

ISBN: (纸本)0819427381

Graphical presentation of data to support decision making has a long history, going back to the earliest use of maps and charts. Continued advances in technology have enabled development of a succession of powerful tools to support decision-making, providing visualization of geographically-referenced observations. Early geographic information systems (GIS) were succeeded by powerful (and affordable) systems. Military command and control systems, and air traffic control systems. demonstrated the value of visualization of real-time data in time-critical decision-making. Decision support systems today combine the functions of a GIS and aspects of command and control systems in a powerful and affordable context for real-time decision-making. The REINAS system is presented as a prototype of such a real-time decision-support systems exploiting real-time geographically-referenced measurements.

关键词： data modeling visualization Geographic information systems databases Decision support systems Control systems visual process modeling Computing systems data storage Systems modeling

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Towards a Highly Interactive Web exploration of Spatial and Historical Aggregations from data Marts

Towards a Highly Interactive Web Exploration of Spatial and ...

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International conference on Advanced Geographic Information Systems and Web Services

作者： Silva, Sonia Fernandes Etruria Telemat Srl Siena Italy

ISBN: (纸本)9781424433636

With the recent advances in the area of WebGIS and Spatial OLAP, new approaches include geographical display and navigation during the explorative analysis of multidimensional data. Such geographical displays can be enriched with visual diagrams for effective data exploration for decision-making. Within this context, we developed a web tool which provides visual interaction with geo-referenced multidimensional data. The implementation of such tool is based on a new approach that puts together some existing techniques in the literature for data exploration and optimization. As a consequence, the tool enables the end-user to remotely create and explore several interactive visual reports of summarized data almost instantaneously. In this paper, we introduce this integrated approach and show its use for interactive web exploration of spatial and historical aggregations from data marts.

关键词： interactive web exploration geo-referenced cubes data marts spatio-temporal indexing spatial OLAP

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SOM Flow: Guided Exploratory Cluster analysis with Self-Organizing Maps and Analytic Provenance

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 120-130页

作者： Sacha, Dominik Kraus, Matthias Bernard, Juergen Behrisch, Michael Schreck, Tobias Asano, Yuki Keim, Daniel A. Univ Konstanz Constance Germany Tech Univ Darmstadt Darmstadt Germany Graz Univ Technol Graz Austria Univ Tubingen Tubingen Germany

Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage visual Analytics (vA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

关键词： visual Analytics Interaction visual Cluster analysis Quality Metrics Guidance Self -Organizing Maps Time Series

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Interactive data exploration through Multiple visual Contexts with Different data Models and Dimensions 21

Interactive Data Exploration through Multiple Visual Context...

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21st International conference on Information visualisation (Iv)

作者： Phi Giang Pham Huang, Mao Lin Quang vinh Nguyen Univ Technol Sydney NSW Australia Cantho Tech Econ Coll Can Tho Vietnam Western Sydney Univ Sydney NSW Australia

ISBN: (纸本)9781538608319

visual analytics plays a key role in bringing insights to audiences who are interested and dedicated in data exploration. In the area of relational data, many advanced visualization tools and frameworks are proposed in order to dealing with such data features. However, the majority of those have not greatly considered the whole process from data-model mining to query utilizing on dimensions and data values, which might cause interruption to exploration activities. This paper presents a new interactive exploration framework for relational data analysis through automatic interconnection of data models, data dimensions and data values. The basic idea is to construct a relative and switchable chain of those context representations by integrating our previous techniques on node-link, parallel coordinate and scatterplot graphics. This approach enables users to flexibly make relative queries on desired contexts at any stage of exploration for deep data understanding. The result from a typical case study for the framework demonstration indicates that our approach is able to handle the addressed challenge.

关键词： visual analytics dynamic visual query interactive user interface

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The match score image: A visualization tool for image query refinement

The match score image: A visualization tool for image query ...

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conference on visual data exploration and analysis v

作者： Bergman, LD Castelli, v IBM Corp Thomas J Watson Res Ctr Yorktown Heights NY 10598 USA

ISBN: (纸本)0819427381

We present a visualization technique designed to facilitate iterative refinement of content-based image queries, particularly example-based specification. The technique operates on scores produced by region-based matching algorithms, including texture matching and template matching. By mapping match scores to color, then compositing with the original image, we provide the user with the "goodness" of match for each region and simultaneously with the original image information. There are several ways in which the match score image can be used to enhance the query refinement process including: facilitating the selection of both positive and negative examples, guiding the selection of thresholds, and enabling exploration of the effect of other parameter values on match algorithm performance. The usability of this visualization technique is highly dependent on choice of score-to-color mapping parameters including continuous vs. discrete, hue range, saturation, lightness, and transparency. We provide some heuristics for selecting these values. Although usable for photographic images, the match score image is particularly useful in application domains such as remote sensing and medical imaging, where particular subregions of large images are sought, rather than entire images.

关键词： query refinement visualization image search

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BrainTrawler: A visual analytics framework for iterative exploration of heterogeneous big brain data

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COMPUTERS & GRAPHICS-UK 2019年 82卷 304-320页

作者： Ganglberger, Florian Swoboda, Nicolas Frauenstein, Lisa Kaczanowska, Joanna Haubensak, Wulf Buehler, Katja VRVis Res Ctr Donau City Str 11-13 A-1220 Vienna Austria Vienna Bioctr VBC Res Inst Mol Pathol IMP Campus Vienna Bioctr 1 A-1030 Vienna Austria

In recent years, big brain-initiatives and consortia have created vast resources of publicly available brain data that can be used by neuroscientists for their own research experiments. This includes microscale connectivity data brain-network graphs with billions of edges and vast spatial gene expression resources the representation of tens of thousands genes in brain space. Their joint analysis for higher order relations in structural or functional neuroanatomy would enable the genetic dissection of brain networks on a genome-wide scale. Current experimental workflows involve only time-consuming manual aggregation and extensive graph theoretical analysis of data from different sources, which rarely provide spatial context to operate continuously on different scales. In this paper, we propose BrainTrawler, a task-driven, web-based framework that incorporates visual analytics methods to explore heterogeneous neurobiological data. It facilitates spatial indexing to query large-scale voxel-level connectivity data and gene expression collections in real-time. Relating data to the hierarchical structure of common anatomical atlases enables the retrieval on different anatomical levels. Together with intuitive network visualization, iterative visual queries, and quantitative information this allows the genetic dissection of multimodal networks on local/global scales in a spatial context. We demonstrate the relevance of our approach for neuroscience by exploring social-behavior and memory/learning related functional neuroanatomy in mice. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Big data Networks Neuroscience Gene expression Brain parcellation visual queries

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