Title:
PIVE: A Per-Iteration Visualization Environment for Supporting Real-time Interactions with Computational Methods
PIVE: A Per-Iteration Visualization Environment for Supporting Real-time Interactions with Computational Methods
dc.contributor.author | Choo, Jaegul | |
dc.contributor.author | Lee, Changhyun | |
dc.contributor.author | Park, Haesun | |
dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | en_US |
dc.contributor.corporatename | Georgia Institute of Technology. School of Computational Science and Engineering | en_US |
dc.date.accessioned | 2013-10-23T21:08:43Z | |
dc.date.available | 2013-10-23T21:08:43Z | |
dc.date.issued | 2013 | |
dc.description | Research areas: Machine learning, Data mining, Information visualization, Visual analytics. | en_US |
dc.description.abstract | Visual analytics has been gaining increasing interest due to its fascinating characteristic that leverages both humans’ visual perception and the power of computing. Although various computational methods are being proposed, they do not properly support visual analytics. One of the biggest obstacles towards their real-time visual analytic integration is their high computational complexity. As a way to tackle this problem, this paper presents PIVE, a Per-Iteration Visualization Environment for supporting real-time interactive visualization with computational methods. The main idea behind PIVE is that most advanced computational methods work by refining the solution iteratively. By visually delivering the result from each iteration to users, the proposed framework enables users to quickly acquire the information that the computational method provides as well as the ability to perform continuous interactions with them in real time. We show the effectiveness of PIVE in terms of real-time visualization and interaction capabilities by customizing various dimension reduction methods such as principal component analysis, multidimensional scaling, and t-distributed stochastic neighborhood embedding, and clustering method s such as k-means and latent Dirichlet allocation. | en_US |
dc.embargo.terms | null | en_US |
dc.identifier.uri | http://hdl.handle.net/1853/49250 | |
dc.language.iso | en_US | en_US |
dc.publisher | Georgia Institute of Technology | en_US |
dc.relation.ispartofseries | CSE Technical Reports ; GT-CSE-13-06 | en_US |
dc.subject | Clustering | en_US |
dc.subject | Dimension reduction | en_US |
dc.subject | Interaction | en_US |
dc.subject | Multi-threading | en_US |
dc.subject | Real-time | en_US |
dc.subject | Visual analytics | en_US |
dc.subject | Visualization | en_US |
dc.title | PIVE: A Per-Iteration Visualization Environment for Supporting Real-time Interactions with Computational Methods | en_US |
dc.type | Text | |
dc.type.genre | Technical Report | |
dspace.entity.type | Publication | |
local.contributor.author | Park, Haesun | |
local.contributor.corporatename | College of Computing | |
local.contributor.corporatename | School of Computational Science and Engineering | |
local.relation.ispartofseries | College of Computing Technical Report Series | |
local.relation.ispartofseries | School of Computational Science and Engineering Technical Report Series | |
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relation.isSeriesOfPublication | 35c9e8fc-dd67-4201-b1d5-016381ef65b8 | |
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