Defining and applying knowledge conversion processes to a visual analytics system

Wang, Xiaoyu; Jeong, Dong Hyun; Dou, Wenwen; Lee, Seok-Won; Ribarsky, William; Chang, Remco

doi:10.1016/j.cag.2009.06.004

Cited by 43 publications

(52 citation statements)

References 14 publications

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“…Although there is no definitive way to achieve complete knowledge transfer, existing research that has demonstrated how to incorporate visualization with domain specific knowledge [XGH06, GNRM08]. To achieve similar knowledge externalization, we propose a tight integration of the visualization with an ontological knowledge structure that could interactively capture and store the user's interactions and translate them into domain knowledge [WJD*09]. This externalization could further be used in training new managers, communicating with others, and reporting decisions.…”

Section: Discussionmentioning

confidence: 99%

An Interactive Visual Analytics System for Bridge Management

Wang

Dou

Chen

et al. 2010

Computer Graphics Forum

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Bridges deteriorate over their life cycles and require continuous maintenance to ensure their structural integrity, and in turn, the safety of the public. Maintaining bridges is a multi-faceted operation that requires both domain knowledge and analytics techniques over large data sources. Although most existing bridge management systems (BMS) are very efficient at data storage, they are not as effective at providing analytical capabilities or as flexible at supporting different inspection technologies. In this paper, we present a visual analytics system that extends the capability of current BMSs. Based on a nation-wide survey and our interviews with bridge managers, we designed our system to be customizable so that it can provide interactive exploration, information correlation, and domainoriented data analysis. When tested by bridge managers of the U.S. Department of Transportation, we validated that our system provides bridge managers with the necessary features for performing in-depth analysis of bridges from a variety of perspectives that are in accordance to their typical workflow.

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Section: Discussionmentioning

confidence: 99%

An Interactive Visual Analytics System for Bridge Management

Wang

Dou

Chen

et al. 2010

Computer Graphics Forum

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“…Chen et al (Chen et al 2009) adapt these processes for information visualization. Wang et al (Wang et al 2009) as well as Federico et al (Federico et al 2017) further substantiate the concept of knowledge conversion to visual analytics with a special focus on explicit knowledge. The four conversion processes are namely: Internalization, Externalization, Combination, and Collaboration.…”

Section: Knowledge-assisted Visual Analyticsmentioning

confidence: 98%

Graph-based visual analytics for cyber threat intelligence

2018

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The ever-increasing amount of major security incidents has led to an emerging interest in cooperative approaches to encounter cyber threats. To enable cooperation in detecting and preventing attacks it is an inevitable necessity to have structured and standardized formats to describe an incident. Corresponding formats are complex and of an extensive nature as they are often designed for automated processing and exchange. These characteristics hamper the readability and, therefore, prevent humans from understanding the documented incident. This is a major problem since the success and effectiveness of any security measure rely heavily on the contribution of security experts. To meet these shortcomings we propose a visual analytics concept enabling security experts to analyze and enrich semi-structured cyber threat intelligence information. Our approach combines an innovative way of persisting this data with an interactive visualization component to analyze and edit the threat information. We demonstrate the feasibility of our concept using the Structured Threat Information eXpression, the state-ofthe-art format for reporting cyber security issues.

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“…The nine desiderata are: D1: Explicit knowledge, by definition, resides in the machine space and is machine interpretable [15,47]. Structured forms of knowledge representation allow the VA environment to reason about relationships within the knowledge (e.g., controlled vocabularies with same/different, taxonomies with hierarchical, or ontologies with custom relationships).…”

Section: Desideratamentioning

confidence: 99%

Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics

Rind¹,

Wagner²,

Aigner³

2019

2019 IEEE Workshop on Visual Analytics in Healthcare (VAHC)

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Clinicians and other analysts working with healthcare data are in need for better support to cope with large and complex data. While an increasing number of visual analytics environments integrates explicit domain knowledge as a means to deliver a precise representation of the available data, theoretical work so far has focused on the role of knowledge in the visual analytics process. There has been little discussion about how such explicit domain knowledge can be structured in a generalized framework. This paper collects desiderata for such a structural framework, proposes how to address these desiderata based on the model of linked data, and demonstrates the applicability in a visual analytics environment for physiotherapy.

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Defining and applying knowledge conversion processes to a visual analytics system

Cited by 43 publications

References 14 publications

An Interactive Visual Analytics System for Bridge Management

An Interactive Visual Analytics System for Bridge Management

Graph-based visual analytics for cyber threat intelligence

Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics

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