AitF: Collaborative Research: Fast, Accurate, and Practical: Adaptive Sublinear Algorithms for Scalable Visualization

AitF：协作研究：快速、准确和实用：用于可扩展可视化的自适应次线性算法

• 批准号: 1733796
• 负责人: Ilias Diakonikolas
• 金额: $ 23.3万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1733796&HistoricalAwards=false
• 关键词: AitF; Collaborative; Research; Fast; Accurate

With the wealth of data being generated in every sphere of human endeavor, data exploration--analyzing, understanding, and extracting value from data--has become absolutely vital. Data visualization is by far the most common data exploration mechanism, used by novice and expert data analysts alike. Yet data visualization on increasingly larger datasets remains difficult: even simple visualizations of a large dataset can be slow and non-interactive, while visualizations of a sampled fraction of a dataset can mislead an analyst. The project aims to develop FastViz, a scalable visualization engine, that will not only enable visualization on datasets that are orders of magnitude larger in the same time, but also ensure the resulting visualizations satisfy key properties essential for correct analysis by end-users. To ensure immediate utilization, FastViz will be applied to three real-world application domains: battery science, advertising analysis, and genomic data analysis, and implemented in Zenvisage, an open-source visual exploration platform developed by the PIs. Students in the project gain invaluable experience in combining the algorithmic and systems considerations that enable data exploration. FastViz's development is driven by simultaneous investigation of systems considerations, such as indexing and storage techniques that enable various forms of online sampling, and algorithmic considerations for (a) visualization generation, where the goal is to produce incrementally improving visualizations in which the important features are displayed first, and (b) visualization selection, where the goal is to select, from a collection of as yet not generated visualizations, those that that satisfy desired criteria. On the systems front, FastViz will leverage and contribute back to recent developments on online sampling systems that enable the use of more powerful sampling modalities. On the algorithms front, FastViz will draw ideas from testing, distribution learning, and sublinear algorithms literature that, to the best knowledge of the PIs, have not been adapted in practice. The algorithms developed will obey optimality guarantees, and wherever possible, instance-optimality guarantees, ensuring that they will adapt to data characteristics in the most efficient way possible. The project will lead to a better understanding of the interplay between sampling algorithms development and systems design, facilitating the adoption of more realistic models and algorithms on the one hand, and the development of more powerful sampling engines that enable the models required within the algorithms.

随着人类活动的各个领域产生大量数据，数据探索（分析、理解数据并从数据中提取价值）变得绝对重要。数据可视化是迄今为止最常见的数据探索机制，新手和专家数据分析师都在使用。然而，越来越大的数据集上的数据可视化仍然很困难：即使是大型数据集的简单可视化也可能很慢且非交互式，而数据集采样部分的可视化可能会误导分析师。该项目旨在开发 FastViz，这是一种可扩展的可视化引擎，它不仅能够同时实现大数量级数据集的可视化，而且还能确保生成的可视化结果满足最终用户正确分析所必需的关键属性。为了确保立即使用，FastViz 将应用于三个实际应用领域：电池科学、广告分析和基因组数据分析，并在 PI 开发的开源视觉探索平台 Zenvisage 中实施。 该项目的学生在结合支持数据探索的算法和系统考虑方面获得了宝贵的经验。 FastViz 的开发是由对系统考虑因素的同时调查驱动的，例如支持各种形式在线采样的索引和存储技术，以及 (a) 可视化生成的算法考虑因素，其目标是产生逐步改进的可视化，其中重要特征是首先显示，(b) 可视化选择，其目标是从尚未生成的可视化集合中选择满足所需标准的可视化。在系统方面，FastViz 将利用在线采样系统的最新发展并做出贡献，从而支持使用更强大的采样方式。 在算法方面，FastViz 将从测试、分布学习和次线性算法文献中汲取灵感，据 PI 所知，这些文献尚未在实践中得到应用。 开发的算法将遵守最优性保证，并尽可能遵守实例最优性保证，确保它们以最有效的方式适应数据特征。 该项目将有助于更好地理解采样算法开发和系统设计之间的相互作用，一方面促进采用更现实的模型和算法，另一方面开发更强大的采样引擎以实现算法中所需的模型。