A matter of time: Applying a data–users–tasks design triangle to visual analytics of time-oriented data

Miksch, Silvia; Aigner, Wolfgang

doi:10.1016/j.cag.2013.11.002

Cited by 126 publications

(93 citation statements)

References 9 publications

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“…In Wagner et al [55], the authors surveyed tools for behavior-based malware analysis in addition to visual representations best suited to various domain challenges. Through a data-users-tasks analysis [33], they ascertained that the parse tree of a grammar such as the one generated by the Sequitur algorithm, can be abstracted to a directed acyclic graph, where each node represents part of a sequence.…”

Section: Visual Analyticsmentioning

confidence: 99%

SEQUIN: a grammar inference framework for analyzing malicious system behavior

Luh

Schramm

Wagner

et al. 2018

J Comput Virol Hack Tech

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Targeted attacks on IT systems are a rising threat to the confidentiality of sensitive data and the availability of critical systems. The emergence of Advanced Persistent Threats (APTs) made it paramount to fully understand the particulars of such attacks in order to improve or devise effective defense mechanisms. Grammar inference paired with visual analytics (VA) techniques offers a powerful foundation for the automated extraction of behavioral patterns from sequential event traces. To facilitate the interpretation and analysis of APTs, we present SEQUIN, a grammar inference system based on the Sequitur compression algorithm that constructs a context-free grammar (CFG) from string-based input data. In addition to recursive rule extraction, we expanded the procedure through automated assessment routines capable of dealing with multiple input sources and types. This automated assessment enables the accurate identification of interesting frequent or anomalous patterns in sequential corpora of arbitrary quantity and origin. On the formal side, we extended the CFG with attributes that help describe the extracted (malicious) actions. Discovery-focused pattern visualization of the output is provided by our dedicated KAMAS VA prototype.

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Section: Visual Analyticsmentioning

confidence: 99%

SEQUIN: a grammar inference framework for analyzing malicious system behavior

Luh

Schramm

Wagner

et al. 2018

J Comput Virol Hack Tech

View full text Add to dashboard Cite

show abstract

“…Scientists face the task of uniting the abstract world of data and the physical world through visual representation. Meanwhile, visualization-based tools should fulfill three requirements [102,103]: expressiveness (demonstrate exactly the information contained in the data), effectiveness (related to cognitive capabilities of human visual system) and appropriateness (cost-value ratio for visualization benefit assessment). Experience of previously used techniques can be repurposed to achieve more beneficial and novel goals in Big Data perception and representation.…”

Section: Big Data Challengesmentioning

confidence: 99%

Visualizing Big Data with augmented and virtual reality: challenges and research agenda

Olshannikova

Ometov

Koucheryavy

et al. 2015

Journal of Big Data

227

117

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“…More recently, Brehmer et al, Schulz and Nocke, and Rind et al proposed multi-level task frameworks that can aid in creating complete task descriptions regardless of domain [33], [34], [35]. At the same time, specific task taxonomies were defined for visualizations of graph-data [36], group-level graphs [37], multidimensional data visualization [38], [39], and temporal data [40]. Task taxonomies and frameworks help define visualization requirements that drive visualization research [7], and support evaluation by supplying benchmark tasks [8], [41], [42].…”

Section: Related Workmentioning

confidence: 99%

A Data Model and Task Space for Data of Interest (DOI) Eye-Tracking Analyses

Jianu

Alam

2018

IEEE Trans. Visual. Comput. Graphics

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-Eye-tracking data is traditionally analyzed by looking at where on a visual stimulus subjects fixate, or, to facilitate more advanced analyses, by using area-of-interests (AOI) defined onto visual stimuli. Recently, there is increasing interest in methods that capture what users are looking at rather than where they are looking. By instrumenting visualization code that transforms a data model into visual content, gaze coordinates reported by an eye-tracker can be mapped directly to granular data shown on the screen, producing temporal sequences of data objects that subjects viewed in an experiment. Such data collection, which is called gaze to object mapping (GTOM) or data-of-interest analysis (DOI), can be done reliably with limited overhead and can facilitate research workflows not previously possible. Our paper contributes to establishing a foundation of DOI analyses by defining a DOI data model and highlighting its differences to AOI data in structure and scale; by defining and exemplifying a space of DOI enabled tasks; by describing three concrete examples of DOI experimentation in three different domains; and by discussing immediate research challenges in creating a framework of visual support for DOI experimentation and analysis. Permanent repository link

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A matter of time: Applying a data–users–tasks design triangle to visual analytics of time-oriented data

Cited by 126 publications

References 9 publications

SEQUIN: a grammar inference framework for analyzing malicious system behavior

SEQUIN: a grammar inference framework for analyzing malicious system behavior

Visualizing Big Data with augmented and virtual reality: challenges and research agenda

A Data Model and Task Space for Data of Interest (DOI) Eye-Tracking Analyses

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