Dynamic Visual Analytics—Facing the Real-Time Challenge

Mansmann, Florian; Fischer, Fabian; Keim, Daniel A.

doi:10.1007/978-1-4471-2804-5_5

Cited by 12 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Network security is one such application field that is characterized not only by big data but also by real-time analysis needs (cf. Mansmann et al 5 ). However, apart from network security, other application fields with big data characteristics such as high-performance computing, 6,7 movement analysis, 8 spatiotemporal text analytics on twitter messages, 9 or news analysis 10 have demonstrated the benefits of visual analytics methods for solving their problems.…”

Section: Related Workmentioning

confidence: 98%

BANKSAFE: Visual analytics for big data in large-scale computer networks

Fischer

Fuchs

Mansmann

et al. 2013

Information Visualization

Self Cite

View full text Add to dashboard Cite

The enormous growth of data in the last decades led to a wide variety of different database technologies. Nowadays, we are capable of storing vast amounts of structured and unstructured data. To address the challenge of exploring and making sense out of big data using visual analytics, the tight integration of such backend services is needed. In this article, we introduce BANKSAFE, which was built for the VAST Challenge 2012 and won the outstanding comprehensive submission award. BANKSAFE is based on modern database technologies and is capable of visually analyzing vast amounts of monitoring data and security-related datasets of large-scale computer networks. To better describe and demonstrate the visualizations, we utilize the Visual Analytics Science and Technology (VAST) Challenge 2012 as case study. Additionally, we discuss lessons learned during the design and development of BANKSAFE, which are also applicable to other visual analytics applications for big data.

show abstract

Section: Related Workmentioning

confidence: 98%

BANKSAFE: Visual analytics for big data in large-scale computer networks

Fischer

Fuchs

Mansmann

et al. 2013

Information Visualization

Self Cite

View full text Add to dashboard Cite

show abstract

“…Because of its volume, video data are generally processed as a stream and can therefore be considered dynamic data, regardless of whether it is recorded historic data (offline) or real-time data from a live capture device (online). Hence, many aspects of dynamic visual analytics 50 also apply to video visual analytics. Despite this overlap, there are fundamental differences between video visual analytics and dynamic visual analytics as it is defined by Mansmann et al 50 They especially consider dynamic visual analytics in the context of real-time tasks, such as monitoring of network traffic.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, many aspects of dynamic visual analytics 50 also apply to video visual analytics. Despite this overlap, there are fundamental differences between video visual analytics and dynamic visual analytics as it is defined by Mansmann et al 50 They especially consider dynamic visual analytics in the context of real-time tasks, such as monitoring of network traffic. However, video visual analytics inherently applies to dynamic data, regardless of the origin of the data.…”

Section: Related Workmentioning

confidence: 99%

Scalable video visual analytics

Höferlin

Heidemann

et al. 2013

Information Visualization

View full text Add to dashboard Cite

Video visual analytics is the research field that addresses scalable and reliable analysis of video data. The vast amount of video data in typical analysis tasks renders manual analysis by watching the video data impractical. However, automatic evaluation of video material is not reliable enough, especially when it comes to semantic abstraction from the video signal. In this article, we describe the video visual analytics method that combines the complementary strengths of human recognition and machine processing. After inspecting the challenges of scalable video analysis, we derive the main components of visual analytics for video data. Based on these components, we present our video visual analytics system that has its origins in our IEEE VAST Challenge 2009 participation.

show abstract

“…Existing surveys on state‐of‐the‐art streaming data analysis have focused on techniques for mining patterns [ILG07] or methods for addressing the problem of scale [BHKP10, Joy09]. Researchers have also looked at the challenges for developing visual analytics methods [MFK12], as visualization techniques alone might not be able to solve many challenges associated with interactive streaming analysis at scale. In a complementary approach to these studies, we aim to understand the human‐centred streaming‐specific goals cutting across different domains, how they can be translated into visualization tasks, and how state‐of‐the‐art visual representations are adapted to influence change perception in high‐velocity streaming environments.…”

Section: Introductionmentioning

confidence: 99%

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

Dasgupta

Arendt

Franklin

et al. 2017

Computer Graphics Forum

View full text Add to dashboard Cite

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.

show abstract

Dynamic Visual Analytics—Facing the Real-Time Challenge

Cited by 12 publications

References 19 publications

BANKSAFE: Visual analytics for big data in large-scale computer networks

BANKSAFE: Visual analytics for big data in large-scale computer networks

Scalable video visual analytics

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

Contact Info

Product

Resources

About