CAREER: Towards Exploratory Data Science on Spatio-temporal Big Data

职业：走向时空大数据的探索性数据科学

• 批准号: 2046236
• 负责人: Ahmed Eldawy
• 金额: $ 54.31万
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046236&HistoricalAwards=false
• 关键词: CAREER; Towards; Exploratory; Data; Science

The OPEN government data act helped in making hundreds of thousands of datasets publicly available to the scientific community and the general public; geospatial data comprise over 60% of this data. This project describes basic research towards building an end-to-end system that allows data science students and domain scientists to interactively explore spatio-temporal datasets. The overarching goal is to bridge the gap between domain scientists and data providers. On one end, it helps domain scientists in various fields, e.g., agriculture, environmental science, and political science, who have little programming skills to explore and access publicly available geospatial and temporal data. On the other end, it helps data providers, e.g., government agencies, non-profit organizations, and national research labs, to attract more data scientists to exploit and utilize public data. The project will also establish educational activities that encourage domain scientists to use public open geospatial data which will promote the reproducibility of scientific results.This project introduces new research directions that are geared towards building an end-to-end interactive exploratory system that will allow domain scientists to process, analyze, and visualize petabytes of spatio-temporal data. It consists of three research components. First, an interactive query processor provides a real-time answer to exploratory queries so that the user will stay engaged and active which increases the productivity; this innovation systematically studies approximate query processing for large geospatial data and will utilize deep learning to provide accurate error bounds for both vector and raster data for complex spatio-temporal queries. Second, to provide users with an exploratory interface, a spatio-temporal visualization component provides an interactive map-based interface that allows users to explore the spatial and temporal attributes of the data. This visualization component will also provide guided assistance to users when exploring big datasets through a novel recommendation system. Lastly, as the datasets grow in size, a dynamic storage system will continuously consume the new records and update the spatio-temporal indexes, data summaries, and visualizations on top of a distributed storage engine which is inherently immutable, i.e., does not support updates. Additionally, this project will build a working prototype where scientists can interactively explore and share spatio-temporal data and share public open data.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.

开放政府数据法案帮助科学界和公众公开了数十万个数据集;地理空间数据占这些数据的60%以上。该项目描述了建立一个端到端系统的基础研究，该系统允许数据科学专业的学生和领域科学家交互式地探索时空数据集。总体目标是弥合领域科学家和数据提供商之间的差距。一方面，它可以帮助各个领域的领域科学家，例如，农业、环境科学和政治科学，他们几乎没有编程技能来探索和访问公开的地理空间和时间数据。另一方面，它有助于数据提供者，例如，政府机构，非营利组织和国家研究实验室，以吸引更多的数据科学家开发和利用公共数据。该项目还将建立教育活动，鼓励领域科学家使用公共开放地理空间数据，这将促进科学成果的可重复性。该项目引入了新的研究方向，旨在建立一个端到端的交互式探索系统，使领域科学家能够处理，分析和可视化PB级的时空数据。它包括三个研究部分。首先，交互式查询处理器为探索性查询提供实时答案，以便用户保持参与和活跃，从而提高生产力;这项创新系统地研究了大型地理空间数据的近似查询处理，并将利用深度学习为复杂的时空查询提供矢量和栅格数据的准确误差范围。其次，为向用户提供探索性界面，时空可视化组件提供了基于地图的交互式界面，允许用户探索数据的空间和时间属性。这个可视化组件还将通过一个新的推荐系统为用户探索大数据集提供指导性帮助。最后，随着数据集大小的增长，动态存储系统将持续消耗新记录并在固有不可变的分布式存储引擎之上更新时空索引、数据摘要和可视化，即，不支持更新。此外，该项目将建立一个工作原型，科学家可以交互式地探索和共享时空数据，并共享公共开放数据。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Incremental partitioning for efficient spatial data analytics

增量分区以实现高效的空间数据分析

• DOI: 10.14778/3494124.3494150
• 发表时间: 2021
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Vu, Tin;Eldawy, Ahmed;Hristidis, Vagelis;Tsotras, Vassilis
• 通讯作者: Tsotras, Vassilis

Beast: Scalable Exploratory Analytics on Spatio-temporal Data

• DOI: 10.1145/3459637.3481897
• 发表时间: 2021-10
• 期刊: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
• 作者: Ahmed Eldawy;Vagelis Hristidis;Saheli Ghosh;Majid Saeedan;Akil Sevim;A.B. Siddique;Samriddhi Singla;Ganeshram Sivaram;Tin Vu;Yaming Zhang

Less is More: How Fewer Results Improve Progressive Join Query Processing

少即是多：更少的结果如何改进渐进式连接查询处理

• DOI: 10.1145/3603719.3603728
• 发表时间: 2023
• 期刊: ACM
• 作者: Zhang, Xin;Eldawy, Ahmed
• 通讯作者: Eldawy, Ahmed

Viper: Interactive Exploration of Large Satellite Data✱✱

• DOI: 10.1145/3609956.3609966
• 发表时间: 2023-08
• 期刊: Proceedings of the 18th International Symposium on Spatial and Temporal Data
• 作者: Zhuocheng Shang;Ahmed Eldawy

How to make your results reproducible with UCR-star and spider

如何使用 UCR-star 和 Spider 使结果可重现

• DOI: 10.1145/3557994.3565975
• 发表时间: 2022
• 期刊: the 4th ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science
• 作者: Majumder, Tomal;Eldawy, Ahmed
• 通讯作者: Eldawy, Ahmed