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.

随着人类努力的每个领域都产生了大量数据，数据探索（从数据中进行分析，理解和提取价值）绝对至关重要。数据可视化是迄今为止新手和专家数据分析师使用的最常见数据探索机制。然而，越来越大的数据集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是缓慢且非相互作用的，而数据集的采样部分的可视化也可能误导分析师。该项目旨在开发一种可扩展的可视化引擎FastViz，它不仅可以在同一时间更大的数据集上可视化，而且还可以确保所得的可视化满足最终用户正确分析必不可少的关键属性。为了确保立即利用，FastViz将应用于三个现实世界应用领域：电池科学，广告分析和基因组数据分析，并在Zenvisage中实施，这是PIS开发的开源视觉探索平台。 该项目中的学生在结合启用数据探索的算法和系统注意事项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所驱动的，例如索引和存储技术，这些技术能够实现各种形式的在线抽样，以及（a）可视化生成的算法考虑因素，目标是在其中产生逐渐改进的可视化，从而可以将重要特征显示出来，而这些目标是首先显示的，并且可以选择该目标，并且可以选择该目标，并且可以选择该目标，并且可以选择该目标，并且可以选择该目标。 标准。在系统方面，FastViz将利用并回溯到在线抽样系统上的最新发展，以实现更强大的采样方式。 在算法方面，FastViz将从测试，分发学习和sublinear算法文献中提出想法，鉴于PI的最佳知识，在实践中尚未适应。 开发的算法将遵守最优性保证，并在可能的情况下保证实例，以确保它们将以最有效的方式适应数据特征。 该项目将更好地理解采样算法开发与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，以及开发更强大的采样引擎，从而促进算法中所需的模型。