Visual Analysis of Time‐Series Similarities for Anomaly Detection in Sensor Networks

Steiger, Martin; Bernard, Jürgen; Mittelstädt, Sebastian; Lücke-Tieke, Hendrik; Keim, Daniel A.; May, Thorsten; Kohlhammer, Jörn

doi:10.1111/cgf.12396

Cited by 66 publications

(42 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, we picked several colormaps that provide a reasonable compromise between tasks ER2, ER3, SR2 presented by Bernard et al [BSM∗15, Table 3]. For the example in Figure 10, we chose the Mittelstädt et al colormap [SBM∗14]. In the implemented prototype we allow the user to choose between the Bremm et al regular and stretched [BLBS11], TeulingFig3NoWhitening [TSS11], and the presented Mittelstädt et al colormaps.…”

Section: Use Casementioning

confidence: 99%

Focus+Context Exploration of Hierarchical Embeddings

Höllt

Vilanova

Pezzotti

et al. 2019

Computer Graphics Forum

View full text Add to dashboard Cite

Hierarchical embeddings, such as HSNE, address critical visual and computational scalability issues of traditional techniques for dimensionality reduction. The improved scalability comes at the cost of the need for increased user interaction for exploration. In this paper, we provide a solution for the interactive visual Focus+Context exploration of such embeddings. We explain how to integrate embedding parts from different levels of detail, corresponding to focus and context groups, in a joint visualization. We devise an according interaction model that relates typical semantic operations on a Focus+Context visualization with the according changes in the level‐of‐detail‐hierarchy of the embedding, including also a mode for comparative Focus+Context exploration and extend HSNE to incorporate the presented interaction model. In order to demonstrate the effectiveness of our approach, we present a use case based on the visual exploration of multi‐dimensional images.

show abstract

Section: Use Casementioning

confidence: 99%

Focus+Context Exploration of Hierarchical Embeddings

Höllt

Vilanova

Pezzotti

et al. 2019

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

“…The SA‐related analytical tasks can be synthesized into the following main types: SA1 :Summarize information from heterogeneous streaming data sources for identifying causal relationships behind the changing patterns. SA2 :Take domain knowledge into account and let analysts explore dynamic what‐if scenarios. Based on our survey, we found that very few papers (with the exception of [Erb12, FK14, MJR*11, SBM*14]) address situational awareness scenarios. In Figure , we see one such example where interactive feature selection is used for summarizing and reasoning purposes.…”

Section: Problem Characterizationmentioning

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

“…The data set is typically multivariate. Related work is for example performed by Steiger et al [45], who described zoomable glyphs. Viewed from a distance, glyphs are recognizable in shape in color; zooming in brings out the information captured by each glyph at detail level.…”

Section: Status Update Viewmentioning

confidence: 99%

Improving collaboration efficiency via diverse networked mobile devices

Rupprecht

Kasakow

Aurich

et al. 2017

J Multimodal User Interfaces

View full text Add to dashboard Cite

We introduce a framework for distributed or colocated teams to collaborate highly efficiently using diverse mobile devices for design and assessment of complex systems. Our framework enhances the efficiency of collaborations arising in design, simulation or data analysis, including visualization. First, we investigate which requirements on such a collaboration framework exist and which influences between task models have to be taken into account; afterwards we transfer those findings into a prototypical system. The devices provide three views of data to be processed collaboratively: (1) a simulation view; (2) a status report view; and (3) a status update view. These views serve the purpose of providing overview, detail and performance views. A smart watch view shows at-a-glance information, the environment or the process being inspected, possibly influenced by a user. Users can use their mobile devices as control interfaces. The framework is especially effective for combining the synergis- tic, complementary competencies of a team. We describe the design of our framework and discuss specific applications.

show abstract

Visual Analysis of Time‐Series Similarities for Anomaly Detection in Sensor Networks

Abstract: We present a system to analyze time-series data in sensor networks. Our

Cited by 66 publications

References 31 publications

Focus+Context Exploration of Hierarchical Embeddings

Focus+Context Exploration of Hierarchical Embeddings

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

Improving collaboration efficiency via diverse networked mobile devices

Contact Info

Product

Resources

About