AF: Small: Towards better geometric algorithms: Summarizing, partitioning and shrinking data

AF：小：迈向更好的几何算法：汇总、分区和缩小数据

• 批准号: 1421231
• 负责人: Sariel Har-Peled
• 金额: $ 49.04万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421231&HistoricalAwards=false
• 关键词: AF; Small; Towards; better; geometric

New (and not so new) technologies like GPS, LIDAR, smart-phones and apps are now used to collect vast amounts of geometric and geographic data. In many such cases the data is too large to be handled by a single computer, and it needs to be broken up into small chunks, so that it can be handled by a cluster/cloud of computers. This award funds research to develop efficient algorithms for manipulating and summarizing such data. In particular, this award concentrates on the issue of how to partition such data efficiently in a balanced way, so that it can be efficiently processed.The algorithms and insights obtained from the technical work will benefit Computer Science and related disciplines where geometric data and algorithms are widely used. The PI hopes to broaden the scope of Computer Science (and Computational Geometry) by introducing new techniques, that would lead to faster and better algorithms, and potentially new applications of geometric data. The project will support and train at least two new PhD students in Computer Science at UIUC. The PI is committed to popularizing ideas and techniques that would be investigated by this proposal, by giving courses, publishing the research, potentially writing a new book on the topic, and use less convectional new tools to disseminate the research using blogs, social media, and online videos.

GPS、激光雷达、智能手机和应用程序等新技术（并不那么新）现在被用来收集大量的几何和地理数据。 在许多这样的情况下，数据太大而无法由单个计算机处理，并且需要将其分解为小块，以便可以由计算机集群/云来处理。 该奖项资助研究开发有效的算法来操作和总结这些数据。特别是，该奖项专注于如何以平衡的方式有效地划分此类数据，以便高效地处理这些数据。从技术工作中获得的算法和见解将使计算机科学和几何数据和算法广泛使用的相关学科受益。PI希望通过引入新技术来扩大计算机科学（和计算几何）的范围，这将导致更快，更好的算法，以及几何数据的潜在新应用。该项目将支持和培训至少两个新的博士生在UIUC计算机科学。PI致力于推广本提案所研究的思想和技术，通过开设课程，出版研究，可能就此主题撰写新书，并使用较少的常规新工具，使用博客，社交媒体和在线视频传播研究。