VIVIR · VIsual representations of VIew Relations to support effective data analysis on large and high-resolution displays
„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“
- Период
- 2017-10-01 → 2022-01-01
- Финансиране от ЕС
- 263 720 €
- Участници
- 2
- Схема
- MSCA-IF-GF
Линиите свързват координатора с партньорите.
Накратко на български
Визуалните връзки между данни на големи екрани се изследват чрез начини за пространствено организиране на графики, например коя от тях показва част от другата. Това помага на интердисциплинарни екипи да анализират информацията по-ефективно при работа лице в лице.
Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.
Резултати накратко
VIsual representations of VIew Relations to support effective data analysis on large and high-resolution displays
The objective of VIVIR is to conduct state-of-the-art research on supporting collaborative face-to-face data analysis. This is motivated by the increasing need for interdisciplinary teams to collaborate on understanding and analyzing data. In VIVIR, the idea is to help people to do by letting them organize visualizations spatially – akin to how pages of a document might be organized on a table. In doing so, it soon becomes necessary to support people in understanding how these visualizations relate to one another. For example, we might help people understand that the relationship between two visualizations, where one of them shows a subset of the other. VIVIR examines possibilities for showing relationships like these.
Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз
Цел на проекта
In VIVIR, I will conduct state-of-the-art research on supporting collaborative face-to-face data analysis. This is motivated by the increasing need for interdisciplinary teams to collaborate on understanding and analysing data. Additionally, as the scale and complexity of data increase, so does the demand for data-based insights and decision-making. My approach is to empower people who are working with large and complex data, by letting them lay out as many visualization views (in the following, denoted views) as necessary on large displays, and creating specialized meta-visualizations to show relations between these views. These meta-visualizations will allow team workers to be aware of each other’s work and the changing view- and data-relationships as they work. While the potential of view meta-visualizations has been acknowledged , there are currently only a few frequently used and considered essential examples of such meta-visualizations. These might show that data in two views are compared in a third view, or that a view shows a subset of the data shown in another view. Most importantly, there has been no thorough exploration into the power and potential of meta-visualization support for data-driven decision-making. To understand the potential impact of meta-visualizations on data analysis, we need to take a structured approach, to formalize these possibilities, which will improve our abilities to support knowledge worker teams as they face the challenges of analyzing increasingly complex data.In brief, data can be difficult to understand. Creating visualizations of data lets people see their data more clearly. As data size and complexity increases, more views are needed to reveal the information hidden in data. Large displays might be useful to solve this. However, a new problem is emerging – how to be aware of the data relationships, and keep an overview of analysis provenance , findings, and decisions between these multiple views. VIVIR tackles this issue.
Оригинален текст от CORDIS (на английски).
Участници
Връзки
Данни: CORDIS, © Европейски съюз