StreamSqueeze: a dynamic stream visualization for monitoring of event data

Mansmann, Florian; Krstajic, Milos; Fischer, Fabian; Bertini, Enrico

doi:10.1117/12.912372

Cited by 6 publications

(6 citation statements)

References 24 publications

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“…Greenacre developed protocols and tools to add motion to scientific graphics so that high-dimensional data can be visualized dynamically [7]. F. Mansmann et al proposed a screen-filling visualization technique for analyzing dynamic information streams in or close to real-time called Stream-Squeeze [1]. F. Fischer et al presented a system to tackle some of the visualization challenges when analyzing such dynamic event data streams [8].…”

Section: A Data Visualizationmentioning

confidence: 98%

Empirical modelling for dynamic visualization of ICU patient data streams

Vong

Rasmequan

Chinnasarn

et al. 2015

2015 8th Biomedical Engineering International Conference (BMEiCON)

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The visualization of data streams plays an important role in diagnosing anomalies in the human body, particularly in Intensive Care Units (ICU). We propose an unconventional paradigm in computer science called Empirical Modelling that is suitable for the combined visualization and exploration of biomedical data streams. Empirical Modelling for Dynamic Visualization (EMDV) provides a learning space for medical education by simulating the ICU patient data streams using web based technology. EMDV benefits from the principles of EM as building models based on observables, dependency and agency, which promotes flexibility and responsiveness in visualizing biomedical data streams. EMDV integrates biomedical signal from different tools into a single monitor. This allows the reduction of cognitive burden in exploring multiple monitors. Open-ended characteristic of EM environment that can enhance the experience of human-machine interaction is founded to be useful to be used as an education technology. The proposed model has been tested with different kinds of mobile devices. The results have clearly shown that the performance of visualization is largely based on the performance of the devices.Index Terms-ICU data streams, visualization, Empirical Modelling, interaction technique, biomedical signal processing, human machine interaction, education technology. LINTRODUCTIONReal-time observations of streaming data become a critical success factor for many visualization applications [1,4], Many of those applications are using conventional paradigm that encompasses rather fix states of observations. In this research, a well-described definition that conforms to the way human think of the dependencies between observations, has been proposed to create the visualization of data streams. This type of dependencies is conceptually similar to the dependencies between cells in a spreadsheet [2]. Meanwhile, its intertwined relationship could promote a more responsive dynamic visualization that can help human construal process better. With the advances in medical technology, biomedical or clinical data streams are in abundance for exploring and analyzing anomalies or patterns that might lead to early diagnosis. A system that can describe those data streams and provide medical staff with a more flexible user interface for finding anomalies or mathematical patterns could contribute to better healthcare in the future. An example of flexible user interfaces are 978-1-4673-9158-01151$31.00 ©20l5 IEEE

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Section: A Data Visualizationmentioning

confidence: 98%

Empirical modelling for dynamic visualization of ICU patient data streams

Vong

Rasmequan

Chinnasarn

et al. 2015

2015 8th Biomedical Engineering International Conference (BMEiCON)

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“…Mansmann et al . also rely on compaction and aggregation to handle graceful ageing of data in their tool, StreamSqueeze [MKF12]. Newer data are shown with the most detail.…”

Section: Survey Of the State Of The Artmentioning

confidence: 99%

“…The solution presented by Mansmann et al . [MKF12] is notable because it does not employ a fixed‐width sliding window; instead, the tool maintains a representation of all data over time. It addresses the data accumulation problem by reducing the fidelity of the information shown as time elapses.…”

Section: Survey Of the State Of The Artmentioning

confidence: 99%

Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization

Dasgupta

Arendt

Franklin

et al. 2017

Computer Graphics Forum

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Real‐world systems change continuously. In domains such as traffic monitoring or cyber security, such changes occur within short time scales. This results in a streaming data problem and leads to unique challenges for the human in the loop, as analysts have to ingest and make sense of dynamic patterns in real time. While visualizations are being increasingly used by analysts to derive insights from streaming data, we lack a thorough characterization of the human‐centred design problems and a critical analysis of the state‐of‐the‐art solutions that exist for addressing these problems. In this paper, our goal is to fill this gap by studying how the state of the art in streaming data visualization handles the challenges and reflect on the gaps and opportunities. To this end, we have three contributions in this paper: (i) problem characterization for identifying domain‐specific goals and challenges for handling streaming data, (ii) a survey and analysis of the state of the art in streaming data visualization research with a focus on how visualization design meets challenges specific to change perception and (iii) reflections on the design trade‐offs, and an outline of potential research directions for addressing the gaps in the state of the art.

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“…However, most of the current approaches in the literature to gain cyber SA focus on the lower and abstract levels of SA techniques such as vulnerability analysis which may use attack graphs (AGs), alert correlation and intrusion detection techniques [86], analyzing attack trend [106], information flow and taint analysis [94], causality analysis and forensics, damage assessment [103]. However, higher SA level ranging from SA perception to projection are still missing and performed manually by experts which is time consuming and error-prone.…”

Section: Introduction a Motivationmentioning

confidence: 99%

A Survey on Threat Situation Awareness Systems: Framework, Techniques, and Insights

Alavizadeh¹,

Jang-Jaccard²,

Enoch³

et al. 2021

Preprint

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Cyberspace is full of uncertainty in terms of advanced and sophisticated cyber threats which are equipped with novel approaches to learn the system and propagate themselves, such as AI-powered threats. To debilitate these types of threats, a modern and intelligent Cyber Situation Awareness (SA) system need to be developed which has the ability of monitoring and capturing various types of threats, analyzing and devising a plan to avoid further attacks. This paper provides a comprehensive study on the current state-of-the-art in the cyber SA to discuss the following aspects of SA: key design principles, framework, classifications, data collection, and analysis of the techniques, and evaluation methods. Lastly, we highlight misconceptions, insights and limitations of this study and suggest some future work directions to address the limitations.

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StreamSqueeze: a dynamic stream visualization for monitoring of event data

Cited by 6 publications

References 24 publications

Empirical modelling for dynamic visualization of ICU patient data streams

Empirical modelling for dynamic visualization of ICU patient data streams

Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization

A Survey on Threat Situation Awareness Systems: Framework, Techniques, and Insights

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