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Abstract Data containing geospatial semantics, such as geotagged tweets, travel blogs, and crime reports, associates natural language texts with geographical locations. This paper presents a lens‐based visual interaction technique, GTMapLens, to flexibly browse the geo‐text data on a map. It allows users to perform dynamic focus+context exploration by using movable lenses to browse geographical regions, find locations of interest, and perform comparative and drill‐down studies. Geo‐text data is visualized in a way that users can easily perceive the underlying geospatial semantics along with lens moving. Based on a requirement analysis with a cohort of multidisciplinary domain experts, a set of lens interaction techniques are developed including keywords control, path management, context visualization, and snapshot anchors. They allow users to achieve a guided and controllable exploration of geo‐text data. A hierarchical data model enables the interactive lens operations by accelerated data retrieval from a geo‐text database. Evaluation with real‐world datasets is presented to show the usability and effectiveness of GTMapLens.
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BrainEx: Interactive Visual Exploration and Discovery of Sequence Similarity in Brain Signals
Technology advances and lower equipment costs are enabling non-invasive, convenient recording of brain data outside of clinical settings in more real-world environments, and by non-experts. Despite the growing interest in and availability of brain signal datasets, most analytical tools are made for experts in the specific device technology, and have rigid constraints on the type of analysis available. We developed BrainEx to support interactive exploration and discovery within brain signals datasets. BrainEx takes advantage of algorithms that enable fast exploration of complex, large collections of time series data, while being easy to use and learn. This system enables researchers to perform similarity search, explore feature data and natural clustering, and select sequences of interest for future searches and exploration, while also maintaining the usability of a visual tool. In addition to describing the distributed architecture and visual design for BrainEx, this paper reports on a benchmark experiment showing that it outperforms other existing systems for similarity search. Additionally, we report on a preliminary user study in which domain experts used the visual exploration interface and affirmed that it meets the requirements. Finally, it presents a case study using BrainEx to explore real-world, domain-relevant data.
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- Award ID(s):
- 1835307
- PAR ID:
- 10418829
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 6
- Issue:
- EICS
- ISSN:
- 2573-0142
- Page Range / eLocation ID:
- 1 to 41
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3534516
