Empirically measuring soft knowledge in visualization
In this paper, we present an empirical study designed to evaluate the hypothesis that humans’ soft knowledge can enhance the cost-benefit ratio of a visualization process by reducing the potential distortion. In particular, we focused on the impact of three classes of soft knowledge: (i) knowledge a...
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| Format: | Journal article |
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John Wiley & Sons Ltd
2017
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| _version_ | 1797103560910962688 |
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| author | Kijmongkolchai, N Abdul-Rahman, A Chen, M |
| author_facet | Kijmongkolchai, N Abdul-Rahman, A Chen, M |
| author_sort | Kijmongkolchai, N |
| collection | OXFORD |
| description | In this paper, we present an empirical study designed to evaluate the hypothesis that humans’ soft knowledge can enhance the cost-benefit ratio of a visualization process by reducing the potential distortion. In particular, we focused on the impact of three classes of soft knowledge: (i) knowledge about application contexts, (ii) knowledge about the patterns to be observed (i.e., in relation to visualization task), and (iii) knowledge about statistical measures. We mapped these classes into three control variables, and used real-world time series data to construct stimuli. The results of the study confirmed the positive contribution of each class of knowledge towards the reduction of the potential distortion, while the knowledge about the patterns prevents distortion more effectively than the other two classes. |
| first_indexed | 2024-03-07T06:21:50Z |
| format | Journal article |
| id | oxford-uuid:f2f7dc7a-e926-4b9b-aee7-ddff6a20e608 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T06:21:50Z |
| publishDate | 2017 |
| publisher | John Wiley & Sons Ltd |
| record_format | dspace |
| spelling | oxford-uuid:f2f7dc7a-e926-4b9b-aee7-ddff6a20e6082022-03-27T12:08:11ZEmpirically measuring soft knowledge in visualizationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:f2f7dc7a-e926-4b9b-aee7-ddff6a20e608Symplectic Elements at OxfordJohn Wiley & Sons Ltd2017Kijmongkolchai, NAbdul-Rahman, AChen, MIn this paper, we present an empirical study designed to evaluate the hypothesis that humans’ soft knowledge can enhance the cost-benefit ratio of a visualization process by reducing the potential distortion. In particular, we focused on the impact of three classes of soft knowledge: (i) knowledge about application contexts, (ii) knowledge about the patterns to be observed (i.e., in relation to visualization task), and (iii) knowledge about statistical measures. We mapped these classes into three control variables, and used real-world time series data to construct stimuli. The results of the study confirmed the positive contribution of each class of knowledge towards the reduction of the potential distortion, while the knowledge about the patterns prevents distortion more effectively than the other two classes. |
| spellingShingle | Kijmongkolchai, N Abdul-Rahman, A Chen, M Empirically measuring soft knowledge in visualization |
| title | Empirically measuring soft knowledge in visualization |
| title_full | Empirically measuring soft knowledge in visualization |
| title_fullStr | Empirically measuring soft knowledge in visualization |
| title_full_unstemmed | Empirically measuring soft knowledge in visualization |
| title_short | Empirically measuring soft knowledge in visualization |
| title_sort | empirically measuring soft knowledge in visualization |
| work_keys_str_mv | AT kijmongkolchain empiricallymeasuringsoftknowledgeinvisualization AT abdulrahmana empiricallymeasuringsoftknowledgeinvisualization AT chenm empiricallymeasuringsoftknowledgeinvisualization |