基于客户端的可视化分析框架,用于架构约束下的大型时空数据

Guizhen Wang, A. Malik, C. Surakitbanharn, J. Q. Neto, S. Afzal, Siqiao Chen, David Wiszowaty, D. Ebert
{"title":"基于客户端的可视化分析框架,用于架构约束下的大型时空数据","authors":"Guizhen Wang, A. Malik, C. Surakitbanharn, J. Q. Neto, S. Afzal, Siqiao Chen, David Wiszowaty, D. Ebert","doi":"10.1109/DSIA.2017.8339088","DOIUrl":null,"url":null,"abstract":"A primary aim of visual analytics is to provide end-users interactive and scalable environments to facilitate their decision making tasks. Researchers have often utilized several server-client solutions to support interactive data exploration (e.g., building the data cube, parallelizing data processing). However, these solutions can suffer from scalability issues especially in the absence of adequate computation functionality provided by servers. Organizational policies can also prohibit the transfer of data to external data servers because of security or budgetary concerns; thereby, severely limiting the capability of the visual analytic systems. Therefore, in this paper, we propose an interactive client-based visual analytics framework for large-scale spatiotemporal data. The proposed framework follows a sampling based incremental visual analysis approach to sustain the real-time responsiveness, meanwhile, with affordable computation resources in a client machine. General sampling methods [34] preprocess the entire dataset to build data indexing, which can bring the client unaffordable computation overhead. Instead, our framework proposes a novel data management model, using the spatiotemporal clustering pattern to predictively organize and sample data based on historical data acquisition activities. We demonstrate the capabilities and usefulness of our framework by applying it on crime data and Twitter data. We also conduct several experimental evaluations to determine the efficacy of our framework.","PeriodicalId":308968,"journal":{"name":"2017 IEEE Workshop on Data Systems for Interactive Analysis (DSIA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A client-based visual analytics framework for large spatiotemporal data under architectural constraints\",\"authors\":\"Guizhen Wang, A. Malik, C. Surakitbanharn, J. Q. Neto, S. Afzal, Siqiao Chen, David Wiszowaty, D. Ebert\",\"doi\":\"10.1109/DSIA.2017.8339088\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A primary aim of visual analytics is to provide end-users interactive and scalable environments to facilitate their decision making tasks. Researchers have often utilized several server-client solutions to support interactive data exploration (e.g., building the data cube, parallelizing data processing). However, these solutions can suffer from scalability issues especially in the absence of adequate computation functionality provided by servers. Organizational policies can also prohibit the transfer of data to external data servers because of security or budgetary concerns; thereby, severely limiting the capability of the visual analytic systems. Therefore, in this paper, we propose an interactive client-based visual analytics framework for large-scale spatiotemporal data. The proposed framework follows a sampling based incremental visual analysis approach to sustain the real-time responsiveness, meanwhile, with affordable computation resources in a client machine. General sampling methods [34] preprocess the entire dataset to build data indexing, which can bring the client unaffordable computation overhead. Instead, our framework proposes a novel data management model, using the spatiotemporal clustering pattern to predictively organize and sample data based on historical data acquisition activities. We demonstrate the capabilities and usefulness of our framework by applying it on crime data and Twitter data. We also conduct several experimental evaluations to determine the efficacy of our framework.\",\"PeriodicalId\":308968,\"journal\":{\"name\":\"2017 IEEE Workshop on Data Systems for Interactive Analysis (DSIA)\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Workshop on Data Systems for Interactive Analysis (DSIA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DSIA.2017.8339088\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Workshop on Data Systems for Interactive Analysis (DSIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSIA.2017.8339088","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

可视化分析的主要目的是为最终用户提供交互式和可扩展的环境,以促进他们的决策任务。研究人员经常使用几种服务器-客户端解决方案来支持交互式数据探索(例如,构建数据立方体,并行处理数据)。然而,这些解决方案可能会受到可伸缩性问题的影响,尤其是在服务器没有提供足够的计算功能的情况下。出于安全或预算考虑,组织政策还可以禁止将数据传输到外部数据服务器;因此,严重限制了视觉分析系统的能力。因此,在本文中,我们提出了一个交互式的基于客户端的大规模时空数据可视化分析框架。提出的框架遵循基于采样的增量可视化分析方法来维持实时响应,同时在客户端机器上使用可负担的计算资源。一般的采样方法[34]都是对整个数据集进行预处理来构建数据索引,这会给客户端带来难以承受的计算开销。相反,我们的框架提出了一种新的数据管理模型,使用时空聚类模式对基于历史数据采集活动的数据进行预测性组织和采样。我们通过将框架应用于犯罪数据和Twitter数据来展示其功能和有用性。我们还进行了几个实验评估,以确定我们的框架的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A client-based visual analytics framework for large spatiotemporal data under architectural constraints
A primary aim of visual analytics is to provide end-users interactive and scalable environments to facilitate their decision making tasks. Researchers have often utilized several server-client solutions to support interactive data exploration (e.g., building the data cube, parallelizing data processing). However, these solutions can suffer from scalability issues especially in the absence of adequate computation functionality provided by servers. Organizational policies can also prohibit the transfer of data to external data servers because of security or budgetary concerns; thereby, severely limiting the capability of the visual analytic systems. Therefore, in this paper, we propose an interactive client-based visual analytics framework for large-scale spatiotemporal data. The proposed framework follows a sampling based incremental visual analysis approach to sustain the real-time responsiveness, meanwhile, with affordable computation resources in a client machine. General sampling methods [34] preprocess the entire dataset to build data indexing, which can bring the client unaffordable computation overhead. Instead, our framework proposes a novel data management model, using the spatiotemporal clustering pattern to predictively organize and sample data based on historical data acquisition activities. We demonstrate the capabilities and usefulness of our framework by applying it on crime data and Twitter data. We also conduct several experimental evaluations to determine the efficacy of our framework.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信