Knowledge Generation through Human-Centered Information Visualization

Abstract

One important intention of human-centered information visualization is to represent huge amounts of abstract data in a visual representation that allows even users from foreign application domains to interact with the visualization, to understand the underlying data, and finally, to gain new, application-related knowledge. The visualization will help experts as well as non-experts to link previously or isolated knowledge-items in their mental map with new insights.Our approach explicitly supports the process of linking knowledge-items with three concepts. At first, the representation of data items in an ontology categorizes and relates them. Secondly, the use of various visualization techniques visually correlates isolated items by graph-structures, layout, attachment, integration, or hyperlink techniques. Thirdly, the intensive use of visual metaphors relates a known source domain to a less known target domain. In order to realize a scenario of these concepts, we developed a visual interface for non-experts to maintain complex wastewater treatment plants. This domain-specific application is used to give our concepts a meaningful background.

Structuring visual exploratory analysis of skill demand

“…This model aligns job titles and descriptions to skillsets. Its set-based job definitions, grouped into sixs ub-skills, 4 contain information on hundreds of standardised and generic occupation-specific descriptors. Of these, one, under technical skills -programming -is directly relevant to our study.…”

“…Visual analytics provides a valuable tool that complements advanced human perception and analytical ability with automated data processing and mining. This lowers human cognitive load in complex data analysis while improving the recognition of patterns and information hidden within data [1,2,4,28,29], support especially valuable in the analysis of complex, high-dimensional data.…”

“…Designing effective support for complex scenarios and data typically involves the exploration of the impact and use of multiple sub-components [4,12,26,27,29,32], following a process that may involve any or all of the following in an iterative cycle:…”

“…One tool proven to enhance the design and construction of powerful and intuitive visual analytics tools and guide effective use, for experts in visualisation and analytics, domain experts and other (non-domain and non-technical) users, is to build on the framework provided by ontologies describing a domain and/or the analytical process [3,4,35]. Hervás et al [5] formally model information spaces and the user's context, and map this to an information visualisation ontology, to support dynamic adaptation of visual representations of information to users' tasks and context.…”

“…Ontologies provide a tool for structuring information spaces; in interactive visualisation this structure may be used to aid navigation through, querying and interpretation of underlying data content. Further, employing a uniform, underlying data structure supports representation from multiple, coordinated perspectives [3][4][5]. Ontology-guided visual analysis allows the strengths of each approach to complement the other, to enable intuitive yet structured, in-depth yet exhaustive analysis, increasing ability to obtain insight into complex data.…”