CAREER: Scalable Spatial Data Science on User-generated Data

职业：基于用户生成数据的可扩展空间数据科学

• 批准号: 2237348
• 负责人: Amr Magdy
• 金额: $ 53.17万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237348&HistoricalAwards=false
• 关键词: CAREER; Scalable; Spatial; Data; Science

Nowadays, hundreds of millions of human users use the world wide web every day. These users generate tremendous amounts of data related to all aspects of life and contain a lot of information about local societies, human activities, and social behavior. By nature, such human-generated data have a spatial aspect, as the geographic location is inherent in many human activities. This makes such data a rich and up-to-date source for social scientists to study different aspects of modern societies to improve people’s life. However, the excessive amount of such data makes it computationally challenging to process complex analyses and extract meaningful insights at a large scale. The project innovates new technology to analyze spatial aspects of user-generated data at a large scale.This project innovates novel scalable data management techniques, especially query processing techniques, to support spatial data science on large user-generated datasets. The project supports two families of queries that are widely used for spatial analysis of user-generated data: spatial estimation queries and spatial grouping queries. The proposed research on spatial estimation includes learning-assisted modules that incorporate machine learning models to improve spatial scalability and accuracy. The proposed research on spatial grouping scales up the grouping of various spatial data types, including points, lines, and polygons, to provide a variety of building blocks that support various applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

如今，每天都有数以亿计的人类用户使用万维网。这些用户产生了大量与生活各个方面相关的数据，并包含了大量关于当地社会、人类活动和社会行为的信息。从本质上讲，这种人类生成的数据具有空间方面，因为地理位置是许多人类活动所固有的。这使得这些数据成为社会科学家研究现代社会不同方面以改善人们生活的丰富和最新来源。然而，过多的此类数据使得处理复杂的分析和大规模提取有意义的见解在计算上具有挑战性。该项目创新了大规模分析用户生成数据的空间方面的新技术。该项目创新了新颖的可扩展数据管理技术，特别是查询处理技术，以支持大型用户生成数据集的空间数据科学。该项目支持广泛用于用户生成数据的空间分析的两类查询：空间估计查询和空间分组查询。拟议的空间估计研究包括学习辅助模块，这些模块结合了机器学习模型，以提高空间可扩展性和准确性。空间分组的拟议研究扩大了各种空间数据类型的分组，包括点，线和多边形，以提供支持各种应用程序的各种构建块。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

EMP: Max-P Regionalization with Enriched Constraints

EMP：具有丰富约束的 Max-P 区域化

• DOI: 10.1109/icde53745.2022.00189
• 发表时间: 2022
• 期刊: IEEE International Conference on Data Engineering (ICDE
• 作者: Kang, Yunfan;Magdy, Amr
• 通讯作者: Magdy, Amr

DDCEL: Efficient Distributed Doubly Connected Edge List for Large Spatial Networks

• DOI: 10.1109/mdm58254.2023.00029
• 发表时间: 2023-07
• 期刊: 2023 24th IEEE International Conference on Mobile Data Management (MDM)
• 作者: Laila Abdelhafeez;A. Magdy;V. Tsotras

PAGE: Parallel Scalable Regionalization Framework

• DOI: 10.1145/3611011
• 发表时间: 2023-07
• 期刊: ACM Transactions on Spatial Algorithms and Systems
• 影响因子: 1.9
• 作者: Hussah Alrashid;Yongyi Liu;A. Magdy

Statistical Inference for Spatial Regionalization

• DOI: 10.1145/3589132.3625608
• 发表时间: 2023-11
• 期刊: Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems
• 作者: Hussah Alrashid;A. Magdy;Sergio Rey

A Scalable Unified System for Seeding Regionalization Queries

• DOI: 10.1145/3609956.3609980
• 发表时间: 2023-08
• 期刊: Proceedings of the 18th International Symposium on Spatial and Temporal Data
• 作者: Hussah Alrashid;A. Magdy