Improving an interactive visualization of transition systems

Ploeger, Bas; Tankink, Carst

doi:10.1145/1409720.1409739

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…Spatial features are useful for identifying unusual behaviour and potential scenarios of risk. Finally, there are some analogies between graph-based approaches and the MTA model [23,26,27]. Indeed, each axis could be considered as a state (i.e., being in particular room or, more generally, in a set of rooms) and arcs between time axes correspond to edges, i.e.…”

Section: Discussionmentioning

confidence: 99%

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Spatiotemporal data visualisation for homecare monitoring of elderly people

Juárez

Ochotorena

Combi

2015

Artificial Intelligence in Medicine

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Section: Discussionmentioning

confidence: 99%

“…These approaches visualise patterns as state diagrams [24,25]: a state diagram is graph where nodes are states and edges are transitions between them. Similar efforts have been done to visualise information in the form of graphs [23,26,27].…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal data visualisation for homecare monitoring of elderly people

Juárez

Ochotorena

Combi

2015

Artificial Intelligence in Medicine

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“…Other visualization tools deal with various particular concerns. LTSView [51] is the oldest one, and it is still being maintained. It supports the visualization of transition systems that model the behavior of a software.…”

Section: ) Behaviormentioning

confidence: 99%

VISON: An Ontology-Based Approach for Software Visualization Tool Discoverability

Merino

Kozlova

Nierstrasz

et al. 2019

2019 Working Conference on Software Visualization (VISSOFT)

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Although many tools have been presented in the research literature of software visualization, there is little evidence of their adoption. To choose a suitable visualization tool, practitioners need to analyze various characteristics of tools such as their supported software concerns and level of maturity. Indeed, some tools can be prototypes for which the lifespan is expected to be short, whereas others can be fairly mature products that are maintained for a longer time. Although such characteristics are often described in papers, we conjecture that practitioners willing to adopt software visualizations require additional support to discover suitable visualization tools. In this paper, we elaborate on our efforts to provide such support. To this end, we systematically analyzed research papers in the literature of software visualization and curated a catalog of 70 available tools that employ various visualization techniques to support the analysis of multiple software concerns. We further encapsulate these characteristics in an ontology. VISON, our software visualization ontology, captures these semantics as concepts and relationships. We report on early results of usage scenarios that demonstrate how the ontology can support (i) developers to find suitable tools for particular development concerns, and (ii) researchers who propose new software visualization tools to identify a baseline tool for a controlled experiment. arXiv:1908.04090v1 [cs.SE] 12 Aug 2019Step 2. Consider reusing existing ontologies. To the best of our knowledge, this is the first ontology of software visualizations.Step 3. Enumerate important terms in the ontology. We include the characteristics of software visualization and their evaluations, as well as the classifications presented in previous studies [6], [7].

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“…These approaches visualise patterns as state diagrams [19], [20]: a state diagram is graph where nodes are states and edges are transitions between them. Similar efforts have been done to visualize information in the form of graphs [21], [22], [18]. There are some analogies between these approaches and the MTA model.…”

Section: ) Aberrant Behaviourmentioning

confidence: 99%

Multiple Temporal Axes for Visualising the Behaviour of Elders Living Alone

Juárez

Ochotorena

Combi

2013

2013 IEEE International Conference on Healthcare Informatics

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Elders who live alone are vulnerable population at risk. Despite telemedicine and clinical home monitoring are effective to provide health care, accidents at home are still one of the major risk factors. Important efforts have been done in home monitoring systems to detect potential scenarios of risk. However, the complex technological interaction has a negative impact on the user and intrusive or wearable devices are not recommended. Sensorized houses solve this problem, recording the behaviour of elders at home without a direct interaction. However, the detection of health-risk scenarios is a complex issue, since the large amount temporal data registered by sensors must be correctly interpreted. In such a case, Artificial Intelligence techniques have been used to automatically identify abnormal behaviors. Due to the number false positives of these techniques, the human intervention is still necessary. We consider that a semiautomatic approach, abstracting and visualizing the recorded temporal information, is a more suitable approach. In this work, we propose a specialised visualization model that helps system supervisors to identify through visual mining techniques most common scenarios of risk. We illustrate our proposal in a specific home monitoring system.

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Improving an interactive visualization of transition systems

Cited by 6 publications

References 15 publications

Spatiotemporal data visualisation for homecare monitoring of elderly people

Spatiotemporal data visualisation for homecare monitoring of elderly people

VISON: An Ontology-Based Approach for Software Visualization Tool Discoverability

Multiple Temporal Axes for Visualising the Behaviour of Elders Living Alone

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