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| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | LFPeers: Temporal similarity search and result exploration |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Computers & Graphics |
| Publisher | Elsevier |
| Geographical Reach | international |
| ISSN | 0097-8493 |
| Volume | 115 |
| Page Range | 81 - 95 |
| Date | 2023 |
| Abstract Text | In this paper, we introduce a general concept for the analysis of temporal and multivariate data and the system LFPeers that applies this concept to temporal similarity search and results exploration. The conceptual workflow divides the analysis in two phases: a search phase to find the most similar objects to a query object before a time point in the temporal data, and an exploration phase to analyze and contextualize this subset of objects after . LFPeers enables users to search for peers through interactive similarity search and filtering, explore interesting behavior of this peer group, and learn from peers through the assessment of diverging behaviors. We present the conceptual workflow to learn from peers and the LFPeers system with novel interfaces for search and exploration in temporal and multivariate data. An earlier workshop publication for LFPeers included a usage scenario targeting epidemiologists and the public who want to learn from the Covid-19 pandemic and distinguish successful and ineffective measures. In this extended paper, we now show how our concept is generalized and applied by domain experts in two case studies, including a novel case on stocks data. Finally, we reflect on the new state of development and on the insights gained by the experts in the case studies on the search and exploration of temporal data to learn from peers. |
| Digital Object Identifier | 10.1016/j.cag.2023.06.009 |
| Other Identification Number | merlin-id:24320 |
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| Keywords | Computer Graphics and Computer-Aided Design, Human-Computer Interaction, General Engineering, Visual Analytics, Interactive Visual Data Analysis |