Streaming data applications are becoming more common due to the ability of different information sources to continuously capture or produce data, such as sensors and social media. Although there are recent advances, most visualization approaches, particularly Dimensionality Reduction (DR) techniques, cannot be directly applied in such scenarios due to the transient nature of streaming data. A few DR methods currently address this limitation using online or incremental strategies, continuously updating the visualization as data is received. Despite their relative success, most impose the need to store and access the data multiple times to produce a complete projection, not being appropriate for streaming where data continuously grow. Others do not impose such requirements but cannot update the position of the data already projected, potentially resulting in visual artifacts. This paper presents Xtreaming, a novel incremental DR technique that continuously updates the visual representation to reflect new emerging structures or patterns without visiting the high-dimensional data more than once. Our tests show that in streaming scenarios where data is not fully stored in-memory, Xtreaming is competitive in terms of quality compared to other streaming and incremental techniques while being orders of magnitude faster.

由于不同信息源能够持续捕获或生成数据，例如传感器和社交媒体，流数据应用程序变得越来越普遍。尽管有最新进展，但由于流数据的瞬态性质，大多数可视化方法，尤其是降维 (DR) 技术，无法直接应用于此类场景。目前，一些 DR 方法使用在线或增量策略解决了这个限制，在接收到数据时不断更新可视化。尽管它们相对成功，但大多数都需要多次存储和访问数据以生成完整的投影，不适用于数据不断增长的流式传输。其他人不强加这样的要求，但不能更新已经投影的数据的位置，可能会导致视觉伪影。本文提出Xtreaming是一种新颖的增量 DR 技术，可不断更新视觉表示以反映新出现的结构或模式，而无需多次访问高维数据。我们的测试表明，在数据未完全存储在内存中的流媒体场景中，与其他流媒体和增量技术相比，Xtreaming在质量方面具有竞争力，同时速度要快几个数量级。