Lytic: synthesizing high-dimensional algorithmic analysis with domain-agnostic, faceted visual analytics

Edward Clarkson, J. Choo, John Turgeson, R. Decuir, Haesun Park
{"title":"Lytic: synthesizing high-dimensional algorithmic analysis with domain-agnostic, faceted visual analytics","authors":"Edward Clarkson, J. Choo, John Turgeson, R. Decuir, Haesun Park","doi":"10.1145/2501511.2501518","DOIUrl":null,"url":null,"abstract":"We present Lytic, a domain-independent, faceted visual analytic (VA) system for interactive exploration of large datasets. It combines a flexible UI that adapts to arbitrary character-separated value (CSV) datasets with algorithmic preprocessing to compute unsupervised dimension reduction and cluster data from high-dimensional fields. It provides a variety of visualization options that require minimal user effort to configure and a consistent user experience between visualization types and underlying datasets. Filtering, comparison and visualization operations work in concert, allowing users to hop seamlessly between actions and pursue answers to expected and unexpected data hypotheses.","PeriodicalId":126062,"journal":{"name":"Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2501511.2501518","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

We present Lytic, a domain-independent, faceted visual analytic (VA) system for interactive exploration of large datasets. It combines a flexible UI that adapts to arbitrary character-separated value (CSV) datasets with algorithmic preprocessing to compute unsupervised dimension reduction and cluster data from high-dimensional fields. It provides a variety of visualization options that require minimal user effort to configure and a consistent user experience between visualization types and underlying datasets. Filtering, comparison and visualization operations work in concert, allowing users to hop seamlessly between actions and pursue answers to expected and unexpected data hypotheses.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
分析:综合高维算法分析与领域不可知论,面可视化分析
我们提出了Lytic,一个领域独立的、面向面的视觉分析(VA)系统,用于大型数据集的交互式探索。它结合了一个灵活的UI,可以适应任意字符分隔值(CSV)数据集和算法预处理,以计算无监督降维和高维字段的聚类数据。它提供了各种可视化选项,这些选项需要最少的用户配置工作,并且在可视化类型和底层数据集之间提供一致的用户体验。过滤、比较和可视化操作协同工作,允许用户在操作之间无缝跳转,并寻求预期和意外数据假设的答案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Online spatial data analysis and visualization system Lytic: synthesizing high-dimensional algorithmic analysis with domain-agnostic, faceted visual analytics Towards anytime active learning: interrupting experts to reduce annotation costs Zips: mining compressing sequential patterns in streams Methods for exploring and mining tables on Wikipedia
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1