VAStream: A Visual Analytics System for Fast Data Streams
VAStream: A Visual Analytics System for Fast Data Streams
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DOI：
10.1145/3332186.3332256
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发表时间：
2019-07
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通讯作者：
Satya Katragadda;Raju N. Gottumukkala;Siva R. Venna;Nicholas G. Lipari;Shailendra Gaikwad;Murali K. Pusala;Jian Chen;C. Borst;Vijay A. Raghavan;M. Bayoumi
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作者：
Satya Katragadda;Raju N. Gottumukkala;Siva R. Venna;Nicholas G. Lipari;Shailendra Gaikwad;Murali K. Pusala;Jian Chen;C. Borst;Vijay A. Raghavan;M. Bayoumi
处理大容量、高速数据流是许多科学、工程和技术领域中的重要大数据问题。有许多开源分布式流处理和云平台可以大规模提供低延迟流处理，但这些系统的可视化和用户交互组件仅限于可视化流处理结果的结果。可视化分析代表了一种新的分析形式，其中用户具有更多的控制和交互功能，可以动态更改可视化，分析或数据管理流程。VASstream为大数据流处理提供了一个环境，沿着交互式可视化功能。系统环境由硬件和软件模块组成，用于优化流数据工作流程（包括数据摄取、预处理、分析、可视化和协作组件）。两个实时流媒体应用的系统环境进行评估。使用社交媒体流的实时事件检测使用来自诸如Twitter的源的文本数据来检测感兴趣的新兴事件。实时河流传感器网络分析项目使用无监督分类方法对来自美国河流网络的传感器网络流进行分类，以检测水质问题。我们讨论了实现细节，并提供各种流处理操作的流处理应用程序的性能比较结果。
Processing high-volume, high-velocity data streams is an important big data problem in many sciences, engineering, and technology domains. There are many open-source distributed stream processing and cloud platforms that offer low-latency stream processing at scale, but the visualization and user-interaction components of these systems are limited to visualizing the outcome of stream processing results. Visual analysis represents a new form of analysis where the user has more control and interactive capabilities either to dynamically change the visualization, analytics or data management processes. VAStream provides an environment for big data stream processing along with interactive visualization capabilities. The system environment consists of hardware and software modules to optimize streaming data workflow (that includes data ingest, pre-processing, analytics, visualization, and collaboration components). The system environment is evaluated for two real-time streaming applications. The real-time event detection using social media streams uses text data arriving from sources such as Twitter to detect emerging events of interest. The real-time river sensor network analysis project uses unsupervised classification methods to classify sensor network streams arriving from the US river network to detect water quality problems. We discuss implementation details and provide performance comparison results of various individual stream processing operations for both stream processing applications.