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首页> 外文期刊>IBM Journal of Research and Development >Just-in-time interactive analytics: Guiding visual exploration of data
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Just-in-time interactive analytics: Guiding visual exploration of data

机译：实时交互式分析：指导数据的可视化探索

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摘要

The increasing complexity and volume of data mandate tighter integration between analytics and visualization. In this paper, I propose a pattern of integrating of computational and visual analytics techniques, called just-in-time (JIT) interactive analytics. JIT analytics is performed in real-time on data that users are interacting with to guide visual-analytic exploration. Fundamental to JIT analytics is enriching visualizations with annotations that describe semantics of visual features, thereby suggesting to users possible insights to examine further. To accomplish this, JIT analytics needs to 1) identify insights depicted as visual patterns such as clusters, outliers, and trends in visualizations and 2) determine the semantics of such features by considering not only attributes that are being visualized but also other attributes in data. In this paper, I describe the JIT interactive analytics pattern, along with a generic implementation for any type of visualization and data, and provide a particular implementation for point-based visualization of multivariate data. I argue that the pattern provides a useful user experience by elevating the cognitive level of interaction with data from pure perception of visual representations to understanding higher level semantics of data. As such, this supports users in building faster qualitative mental models and accelerating discovery. Furthermore, facilitating insight opens new research opportunities such as visual-analytic action recommendations, improved collaboration, and accessibility.
机译：数据的复杂性和数量不断增加，要求在分析和可视化之间进行更紧密的集成。在本文中，我提出了一种整合计算和视觉分析技术的模式，称为即时（JIT）交互式分析。 JIT分析是对与用户交互的数据实时执行的，以指导视觉分析探索。 JIT分析的基础是，通过描述视觉特征语义的注释来丰富可视化，从而向用户建议可能的见解，以便进一步检查。为了实现这一点，JIT分析需要：1）识别描述为可视化模式的洞察力，例如群集，离群值和可视化趋势，以及2）通过不仅考虑可视化的属性而且还要考虑数据中的其他属性来确定此类功能的语义。在本文中，我描述了JIT交互式分析模式，以及适用于任何类型的可视化和数据的通用实现，并提供了针对多数据的基于点的可视化的特定实现。我认为，该模式通过将与数据交互的认知级别从视觉表示的纯感知提升为理解数据的更高层次的语义，从而提供了有用的用户体验。因此，这支持用户建立更快的定性思维模型并加速发现。此外，促进洞察力还开辟了新的研究机会，例如视觉分析行动建议，改进的协作和可访问性。

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