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

Many-Attribute Decision Making Using Iterative Attribute Subsets

2016 ◽  
Author(s):  
Laura Ziegler ◽  
Kemper Lewis
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Decision Problems ◽  
Trade Off ◽  
Current Design ◽  
Multi Attribute Decision Making

A unique set of cognitive and computational challenges arise in large-scale decision making, in relation to trade-off processing and design space exploration. While several multi-attribute decision making methods exist in the current design literature, many are insufficient or not fully explored for many-attribute decision problems of six or more attributes. To address this scaling in complexity, the methodology presented in this paper strategically elicits preferences over iterative attribute subsets while leveraging principles of the Hypothetical Equivalents and Inequivalents Method (HEIM). A case study demonstrates the effectiveness of the approach in the construction of a systematic representation of preferences and the convergence to a single ‘best’ alternative.

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.

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