GeoLens: Enabling Interactive Visual Analytics over Large-Scale, Multidimensional Geospatial Datasets

2014 ◽  
Author(s):  
Jared Koontz ◽  
Matthew Malensek ◽  
Sangmi Pallickara
Keyword(s):  
Visual Analytics ◽  
Large Scale ◽  
Geospatial Datasets

Visual Analytics Techniques for Exploring the Design Space of Large-Scale High-Radix Networks

2017 ◽  
Author(s):  
Jianping Kelvin Li ◽  
Misbah Mubarak ◽  
Robert B. Ross ◽  
Christopher D. Carothers ◽  
Kwan-Liu Ma
Keyword(s):  
Visual Analytics ◽  
Large Scale ◽  
Design Space ◽  
High Radix

scholarly journals Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data

2019 ◽  
Author(s):  
Robert Krueger ◽  
Johanna Beyer ◽  
Won-Dong Jang ◽  
Nam Wook Kim ◽  
Artem Sokolov ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Visual Analytics ◽  
Cancer Biology ◽  
Large Scale ◽  
Hierarchical Structures ◽  
Image Data ◽  
Automated Analysis ◽  
Cell Types ◽  
High Dimensional ◽  
Exploration Process

AbstractFacetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images of human tumors and tissues. Such images represent the cutting edge of digital histology and promise to revolutionize how diseases such as cancer are studied, diagnosed, and treated. Highly multiplexed tissue images are complex, comprising 109or more pixels, 60-plus channels, and millions of individual cells. This makes manual analysis challenging and error-prone. Existing automated approaches are also inadequate, in large part, because they are unable to effectively exploit the deep knowledge of human tissue biology available to anatomic pathologists. To overcome these challenges, Facetto enables a semi-automated analysis of cell types and states. It integrates unsupervised and supervised learning into the image and feature exploration process and offers tools for analytical provenance. Experts can cluster the data to discover new types of cancer and immune cells and use clustering results to train a convolutional neural network that classifies new cells accordingly. Likewise, the output of classifiers can be clustered to discover aggregate patterns and phenotype subsets. We also introduce a new hierarchical approach to keep track of analysis steps and data subsets created by users; this assists in the identification of cell types. Users can build phenotype trees and interact with the resulting hierarchical structures of both high-dimensional feature and image spaces. We report on use-cases in which domain scientists explore various large-scale fluorescence imaging datasets. We demonstrate how Facetto assists users in steering the clustering and classification process, inspecting analysis results, and gaining new scientific insights into cancer biology.

scholarly journals Visual Analytics of Traffic Congestion Propagation Path with Large Scale Camera Data

2018 ◽  
Vol 27 (5) ◽  
pp. 934-941 ◽  
Author(s):  
Zhenyu Shan ◽  
Zhigeng Pan ◽  
Fengwei Li ◽  
Huihui Xu ◽  
Huihui Xu
Keyword(s):  
Traffic Congestion ◽  
Visual Analytics ◽  
Large Scale ◽  
Propagation Path

scholarly journals Collaborative visual analytics of radio surveys in the Big Data era

2016 ◽  
Vol 12 (S325) ◽  
pp. 311-315 ◽  
Author(s):  
Dany Vohl ◽  
Christopher J. Fluke ◽  
Amr H. Hassan ◽  
David G. Barnes ◽  
Virginia A. Kilborn
Keyword(s):  
Visual Analytics ◽  
Large Scale ◽  
Research Process ◽  
Multidimensional Data ◽  
Asynchronous Collaboration ◽  
Parallel Data ◽  
Display Ecology ◽  
Data Inquiry ◽  
Large Survey ◽  
2D And 3D

AbstractRadio survey datasets comprise an increasing number of individual observations stored as sets of multidimensional data. In large survey projects, astronomers commonly face limitations regarding: 1) interactive visual analytics of sufficiently large subsets of data; 2) synchronous and asynchronous collaboration; and 3) documentation of the discovery workflow. To support collaborative data inquiry, we present encube, a large-scale comparative visual analytics framework. encube can utilise advanced visualization environments such as the CAVE2 (a hybrid 2D and 3D virtual reality environment powered with a 100 Tflop/s GPU-based supercomputer and 84 million pixels) for collaborative analysis of large subsets of data from radio surveys. It can also run on standard desktops, providing a capable visual analytics experience across the display ecology. encube is composed of four primary units enabling compute-intensive processing, advanced visualisation, dynamic interaction, parallel data query, along with data management. Its modularity will make it simple to incorporate astronomical analysis packages and Virtual Observatory capabilities developed within our community. We discuss how encube builds a bridge between high-end display systems (such as CAVE2) and the classical desktop, preserving all traces of the work completed on either platform – allowing the research process to continue wherever you are.

Visual analytics towards axle health of high-speed train based on large-scale scatter image

2019 ◽  
Vol 79 (23-24) ◽  
pp. 16663-16681
Author(s):  
Kunlin Zhang ◽  
Jihui Xu ◽  
Huaiyu Xu ◽  
Ruidan Su
Keyword(s):  
Visual Analytics ◽  
High Speed ◽  
Large Scale ◽  
High Speed Train

scholarly journals BANKSAFE: Visual analytics for big data in large-scale computer networks

2013 ◽  
Vol 14 (1) ◽  
pp. 51-61 ◽  
Author(s):  
Fabian Fischer ◽  
Johannes Fuchs ◽  
Florian Mansmann ◽  
Daniel A Keim
Keyword(s):  
Big Data ◽  
Computer Networks ◽  
Visual Analytics ◽  
Large Scale ◽  
Science And Technology ◽  
Lessons Learned ◽  
Unstructured Data ◽  
Monitoring Data ◽  
Making Sense

The enormous growth of data in the last decades led to a wide variety of different database technologies. Nowadays, we are capable of storing vast amounts of structured and unstructured data. To address the challenge of exploring and making sense out of big data using visual analytics, the tight integration of such backend services is needed. In this article, we introduce BANKSAFE, which was built for the VAST Challenge 2012 and won the outstanding comprehensive submission award. BANKSAFE is based on modern database technologies and is capable of visually analyzing vast amounts of monitoring data and security-related datasets of large-scale computer networks. To better describe and demonstrate the visualizations, we utilize the Visual Analytics Science and Technology (VAST) Challenge 2012 as case study. Additionally, we discuss lessons learned during the design and development of BANKSAFE, which are also applicable to other visual analytics applications for big data.

scholarly journals Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations

2019 ◽  
Author(s):  
Junghoon Chae ◽  
Debsindhu Bhowmik ◽  
Heng Ma ◽  
Arvind Ramanathan ◽  
Chad Steed
Keyword(s):  
Molecular Dynamics ◽  
Visual Analytics ◽  
Large Scale ◽  
Physical Phenomenon ◽  
Living Organism ◽  
Md Simulations ◽  
High Dimensional ◽  
Molecular Characteristics ◽  
Complex Simulation ◽  
Dynamics Simulations

AbstractMolecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).

scholarly journals Vermont: a multi-perspective visual interactive platform for mutational analysis

2017 ◽  
Author(s):  
Alexandre V Fassio ◽  
Pedro M Martins ◽  
Samuel da S Guimarães ◽  
Sócrates S A Junior ◽  
Vagner S Ribeiro ◽  
...  
Keyword(s):  
Protein Function ◽  
Visual Analytics ◽  
Large Scale ◽  
Mutational Analysis ◽  
Structural Parameters ◽  
Point Mutations ◽  
Huge Amount ◽  
Interactive Visualizations ◽  
And Function ◽  
The Impact

AbstractBackgroundA huge amount of data about genomes and sequence variation is available and continues to grow on a large scale, which makes experimentally characterizing these mutations infeasible regarding disease association and effects on protein structure and function. Therefore, reliable computational approaches are needed to support the understanding of mutations and their impacts. Here, we present VERMONT 2.0, a visual interactive platform that combines sequence and structural parameters with interactive visualizations to make the impact of protein point mutations more understandable.ResultsWe aimed to contribute a novel visual analytics oriented method to analyze and gain insight on the impact of protein point mutations. To assess the ability of VERMONT to do this, we visually examined a set of mutations that were experimentally characterized to determine if VERMONT could identify damaging mutations and why they can be considered so.ConclusionsVERMONT allowed us to understand mutations by interpreting position-specific structural and physicochemical properties. Additionally, we note some specific positions we believe have an impact on protein function/structure in the case of mutation.

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