CRII: III: Scalable Noise-filtering and Community Queries on User-generated Data

CRII：III：可扩展的噪声过滤和对用户生成数据的社区查询

• 批准号: 1849971
• 负责人: Amr Magdy
• 金额: $ 17.49万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849971&HistoricalAwards=false
• 关键词: CRII; III; Scalable; Noise; filtering

This project investigates novel indexing and querying techniques that enable scientists to analyze and extract meaningful data from large repositories of user-generated data. The need for such techniques is significant, especially for user-generated social media data, which is the major repository of user-generated data and the largest archived and real-time source of human behavior and information. Thus, scientists are widely using this data in disciplines as disparate as sociology, behavioral sciences, education, spatial sciences, food sciences, medical studies, and political sciences. This project focuses on innovative indexing and querying techniques to enable scientists to effectively exploit user-generated data at a large scale.The planned research adds new data management infrastructure modules to support: (1) Scalable noise filtering queries, a subset of selection queries that are needed repeatedly and are expressed in SQL-based systems as multiple nested queries, which is not efficient for large datasets. To support this, the project investigates techniques for: (a) Advanced query conjunctions, e.g., BUT-NOT and EITHER-XOR, to scale up complex-predicate queries beyond basic search queries that are currently supported in data management systems. (b) Scalable contextual scoring of data records, e.g., based on sentiment or semantics, so irrelevant records are pruned early and the search space is downsized significantly. (2) Scalable community-centric queries that enables scientists to ask queries about communities with large numbers of users while having real-time query response beyond what is currently supported by graph data management technology. The project investigates indexing, query processing, and storage optimization techniques that scale up such queries at a system-level.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.

该项目研究了新的索引和查询技术，使科学家能够从大量用户生成的数据中分析和提取有意义的数据。对这种技术的需求是巨大的，特别是对于用户生成的社交媒体数据，这是用户生成数据的主要存储库，也是人类行为和信息的最大存档和实时来源。因此，科学家们在社会学、行为科学、教育学、空间科学、食品科学、医学研究和政治学等不同学科中广泛使用这些数据。该项目侧重于创新索引和查询技术，使科学家能够有效地大规模利用用户生成的数据。计划中的研究增加了新的数据管理基础架构模块来支持：(1)可扩展的噪声过滤查询，这是重复需要的选择查询的子集，在基于sql的系统中表示为多个嵌套查询，这对于大型数据集来说效率不高。为了支持这一点，该项目研究了以下技术：(a)高级查询连词，例如，BUT-NOT和EITHER-XOR，以扩展复杂谓词查询，超出当前数据管理系统支持的基本搜索查询。(b)可扩展的数据记录上下文评分，例如，基于情感或语义，因此不相关的记录被提前修剪，搜索空间显着缩小。(2)可扩展的以社区为中心的查询，使科学家能够查询拥有大量用户的社区，同时具有超越当前图形数据管理技术所支持的实时查询响应。该项目研究在系统级扩展此类查询的索引、查询处理和存储优化技术。该奖项反映了美国国家科学基金会的法定使命，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

Towards A Unified Framework for Event Detection Applications

迈向事件检测应用程序的统一框架

• DOI: 10.1145/3340964.3340994
• 发表时间: 2019
• 期刊: SSTD '19: Proceedings of the 16th International Symposium on Spatial and Temporal Databases
• 作者: Alghamdi, Rami A.;Magdy, Amr;Mokbel, Mohamed F.
• 通讯作者: Mokbel, Mohamed F.

DiReCT: Disaster Response Coordination with Trusted Volunteers

DiReCT：与值得信赖的志愿者进行灾难响应协调

• DOI: 10.1109/ict-dm47966.2019.9032915
• 发表时间: 2019
• 期刊: 2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM
• 作者: Jahanian, Mohammad;Hasegawa, Toru;Kawabe, Yoshinobu;Koizumi, Yuki;Magdy, Amr;Nishigaki, Masakatsu;Ohki, Tetsushi;Ramakrishnan, K. K.
• 通讯作者: Ramakrishnan, K. K.

Spatio-temporal analysis of meta-data semantics of market shares over large public geosocial media data

大型公共地理社交媒体数据市场份额元数据语义的时空分析

• DOI: 10.1080/17489725.2018.1547428
• 发表时间: 2018
• 期刊: Journal of Location Based Services
• 影响因子: 2.3
• 作者: Almaslukh, Abdulaziz;Magdy, Amr;Rey, Sergio J.
• 通讯作者: Rey, Sergio J.

Scalable Multi-resolution Spatial Visualization for Anthropogenic Litter Data

人为垃圾数据的可扩展多分辨率空间可视化

• DOI: 10.1145/3347146.3359074
• 发表时间: 2019
• 期刊: ACM SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
• 作者: Kang, Yunfan;Zhao, Ziang;Magdy, Amr;Cowger, Win;Gray, Andrew
• 通讯作者: Gray, Andrew

Microblogs data management: a survey

• DOI: 10.1007/s00778-019-00569-6
• 发表时间: 2019-09
• 期刊: The VLDB Journal
• 作者: A. Magdy;Laila Abdelhafeez;Yunfan Kang;Eric Ong;M. Mokbel