Database System Support for Data Visualization

数据可视化的数据库系统支持

• 批准号: RGPIN-2019-06301
• 负责人: Gryz, Jaroslaw
• 金额: $ 2.04万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715992
• 关键词: Database; System; Support; Data; Visualization

One picture is worth a thousand words. The idea of replacing a complex narrative with a single image may have become a cliché in journalism, but it is an absolute requirement in data exploration. After all, one may be able to read a thousand words, but cannot possibly look at, let alone understand, a billion data points. Visualization offers an indispensable tool in understanding the data. The process of data visualization can be described from a high-level perspective as consisting of two steps: processing the data, then applying one of the visualization algorithms to it. There has been significant work done on data visualization over the last 50 years. Interestingly, most of this work concentrated on the second step of the visualization process. This was understandable, as data sets were relatively small and the performance of the visualization tools was often determined by the efficiency of the graphics algorithms. With the advent of large data sets a radical shift in the design of data visualization tools has to be made. Data retrieval and processing time now dominates the performance of the visualization process, so cannot be ignored. Without efficient database support, visualization, in particular, interactive visualization is impossible. The research program we propose addresses this very problem. We will look into different visualization techniques over different types of data and analyze the performance of the data processing engine that pre-processes data to be visualized. We will offer new data processing techniques to improve the performance of the back-end system and integrate them with the front-end visualization step. We expect to build prototypes of such systems and demonstrate experimentally their viability.

一幅画胜过千言万语。替换的想法 只有一个形象的复杂叙事可能已经成为新闻业的陈词滥调， 但这是数据探索中的绝对要求。毕竟，其中一个可能是 能读一千个字，但不可能看，更不用说 明白吗，十亿个数据点。可视化提供了一个不可或缺的工具 在理解数据方面。 这个 数据可视化的过程可以从高层次的角度描述为 包括两个步骤：处理数据，然后应用 它的可视化算法。在数据方面已经做了大量的工作 在过去的50年里，可视化。有趣的是，这项工作的大部分 集中在可视化过程的第二步。这是 可以理解，因为数据集相对较小，并且 可视化工具通常由图形的效率决定 算法。随着大数据集的出现，设计发生了根本性的转变 必须制作数据可视化工具。现在的数据检索和处理时间 控制可视化过程的性能，因此不容忽视。 如果没有有效的数据库支持，可视化，特别是交互 视觉化是不可能的。 我们提出的研究计划解决了这个问题。我们 将针对不同类型的数据研究不同的可视化技术 并对数据处理引擎进行了性能分析 要可视化的数据。我们将提供新的数据处理技术来改进 后端系统的性能，并将其与前端集成 可视化步骤。我们希望建立这样的系统的原型和 从实验上证明了它们的可行性。