Real-Time Visualization of Stream-Based Monitoring Data

被引:2
|
作者
Baumeister, Jan [1 ]
Finkbeiner, Bernd [1 ]
Gumhold, Stefan [2 ]
Schledjewski, Malte [1 ]
机构
[1] CISPA Helmholtz Ctr Informat Secur, D-66123 Saarbrucken, Germany
[2] Tech Univ Dresden, D-01069 Dresden, Germany
来源
关键词
Runtime verification; Stream-based monitoring; Data visualization;
D O I
10.1007/978-3-031-17196-3_21
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Stream-based runtime monitors are used in safety-critical applications such as Unmanned Aerial Systems (UAS) to compute comprehensive statistics and logical assessments of system health that provide the human operator with critical information in hand-over situations. In such applications, a visual display of the monitoring data can be much more helpful than the textual alerts provided by a more traditional user interface. This visualization requires extensive real-time data processing, which includes the synchronization of data from different streams, filtering and aggregation, and priorization and management of user attention. We present a visualization approach for the RTLoLA monitoring framework. Our approach is based on the principle that the necessary data processing is the responsibility of the monitor itself, rather than the responsibility of some external visualization tool. We show how the various aspects of the data transformation can be described as RTLoLA stream equations and linked to the visualization component through a bidirectional synchronous interface. In our experience, this approach leads to highly informative visualizations as well as to understandable and easily maintainable monitoring code.
引用
下载
收藏
页码:325 / 335
页数:11
相关论文
共 50 条
  • [21] A Distributed Real-Time Monitoring Scheme for Air Pressure Stream Data Based on Kafka
    Zhou, Zixiang
    Zhou, Lei
    Chen, Zhiguo
    APPLIED SCIENCES-BASEL, 2024, 14 (12):
  • [22] PRIVMON: A Stream-Based System for Real-Time Privacy Attack Detection for Machine Learning Models
    Ko, Myeongseob
    Yang, Xinyu
    Ji, Zhengjie
    Just, Hoang Anh
    Gao, Peng
    Kumar, Anoop
    Jia, Ruoxi
    PROCEEDINGS OF THE 26TH INTERNATIONAL SYMPOSIUM ON RESEARCH IN ATTACKS, INTRUSIONS AND DEFENSES, RAID 2023, 2023, : 264 - 281
  • [23] Stream-based Machine Learning for Real-time QoE Analysis of Encrypted Video Streaming Traffic
    Seufert, Michael
    Casas, Pedro
    Wehner, Nikolas
    Gang, Li
    Li, Kuang
    PROCEEDINGS OF THE 2019 22ND CONFERENCE ON INNOVATION IN CLOUDS, INTERNET AND NETWORKS AND WORKSHOPS (ICIN), 2019, : 76 - 81
  • [24] Real-Time Monitoring of Road Traffic using Data Stream Mining
    Figueiras, Paulo
    Guerreiro, Guilherme
    Costa, Ruben
    Herga, Zala
    Rosa, Antonia
    Jardim-Goncalves, Ricardo
    2018 IEEE INTERNATIONAL CONFERENCE ON ENGINEERING, TECHNOLOGY AND INNOVATION (ICE/ITMC), 2018,
  • [25] Research on Visualization of Multi-Dimensional Real-Time Traffic Data Stream Based on Cloud Computing
    Jia Chaolong
    Wang Hanning
    Wei Lili
    GREEN INTELLIGENT TRANSPORTATION SYSTEM AND SAFETY, 2016, 138 : 709 - 718
  • [26] Data Is a Stream: Security of Stream-Based Channels
    Fischlin, Marc
    Guenther, Felix
    Marson, Giorgia Azzurra
    Paterson, Kenneth G.
    ADVANCES IN CRYPTOLOGY, PT II, 2015, 9216 : 545 - 564
  • [27] Real-time Dynamic Data Desensitization Method based on Data Stream
    Tian, Bing
    Lv, Shuqing
    Yin, Qilin
    Li, Ning
    Zhang, Yue
    Liu, Ziyan
    PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION SCIENCE AND SYSTEM, AISS 2019, 2019,
  • [28] Real-Time Monitoring of Health Security Attacks with R-based Data Visualization Dashboard
    Shan, Mridula
    14TH ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY, AND HEALTH INFORMATICS, BCB 2023, 2023,
  • [29] Density-Based Clustering for Real-Time Stream Data
    Chen, Yixin
    Tu, Li
    KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2007, : 133 - +
  • [30] Real-time stream data mining based on CanTree and Gtree
    Kim, Jaein
    Hwang, Buhyun
    INFORMATION SCIENCES, 2016, 367 : 512 - 528