EAGER: Data Analytics over Location Based Services

EAGER：基于位置的服务的数据分析

• 批准号: 1745925
• 负责人: Gautam Das
• 金额: $ 19.93万
• 依托单位: University of Texas at Arlington
• 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: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1745925&HistoricalAwards=false
• 关键词: EAGER; Data; Analytics; over; Location

Location Based Services are extremely popular, with millions of users making daily use of mapping services such as Google and Bing maps, as well as location based features integrated into numerous other systems such as Twitter and Yelp. Data analytics over the backend databases of these services can reveal interesting "big picture" information, such as geographic distribution of points of interest and regional variation in user behavior. In this project we show that such interesting data analytics can be performed by users whose access to the databases is limited via the available programming and query interfaces. The research results of this project will impact the nation's higher education system and high-tech industries. The ability to pose high-level analytical queries over location based services is needed by knowledge workers in a wide variety of corporations, governments, and security agencies. Parts of this project are being integrated into teaching, which will potentially attract motivated students to pursue doctoral degrees.The research involves developing a suite of algorithms and techniques for understanding the opportunities and challenges of data analytics over location based services. The various data analytics and mining tasks considered include point and path aggregate estimation as well as dual mining over location based services limited by available data access interfaces. The research makes fundamental advancements to engineering by showing how to integrate theoretically-proven algorithms with application-specific details of real-world location based services. A data analytics prototype is also being developed and will be evaluated over several real-world location based services.

基于位置的服务非常受欢迎，数百万用户每天使用 Google 和 Bing 地图等地图服务，以及集成到 Twitter 和 Yelp 等众多其他系统中的基于位置的功能。对这些服务后端数据库的数据分析可以揭示有趣的“大局”信息，例如兴趣点的地理分布和用户行为的区域变化。在这个项目中，我们展示了这种有趣的数据分析可以由通过可用的编程和查询接口限制对数据库的访问的用户执行。该项目的研究成果将对国家高等教育体系和高新技术产业产生影响。各种公司、政府和安全机构的知识工作者都需要能够对基于位置的服务提出高级分析查询的能力。该项目的部分内容正在融入教学，这将有可能吸引有动力的学生攻读博士学位。该研究涉及开发一套算法和技术，以了解基于位置的服务进行数据分析的机遇和挑战。 考虑的各种数据分析和挖掘任务包括点和路径聚合估计以及受可用数据访问接口限制的基于位置的服务的双重挖掘。 该研究通过展示如何将理论验证的算法与现实世界基于位置的服务的特定应用细节相集成，在工程方面取得了根本性的进步。数据分析原型也正在开发中，并将通过几个基于现实世界位置的服务进行评估。

项目成果

Efficient Construction of Approximate Ad-Hoc ML models Through Materialization and Reuse

• DOI: 10.14778/3236187.3236199
• 发表时间: 2018-07
• 期刊: Proc. VLDB Endow.
• 作者: Sona Hasani;Saravanan Thirumuruganathan;Abolfazl Asudeh;Nick Koudas;Gautam Das

Leveraging Similarity Joins for Signal Reconstruction

• DOI: 10.14778/3231751.3231752
• 发表时间: 2018-06
• 期刊: Proc. VLDB Endow.
• 作者: Abolfazl Asudeh;Azade Nazi;Jees Augustine;Saravanan Thirumuruganathan;Nan Zhang;Gautam Das;D. Srivastava

Efficient Signal Reconstruction for a Broad Range of Applications

• DOI: 10.1145/3371316.3371327
• 发表时间: 2019-11
• 期刊: SIGMOD Rec.
• 作者: Abolfazl Asudeh;Jees Augustine;Azade Nazi;Saravanan Thirumuruganathan;Nan Zhang;Gautam Das;D. Srivastava

DBLOC: Density Based Clustering over LOCation Based Services

• DOI: 10.1145/3183713.3193561
• 发表时间: 2018-05
• 期刊: Proceedings of the 2018 International Conference on Management of Data
• 作者: Yeshwanth D. Gunasekaran;Md. Farhadur Rahman;Sona Hasani;Nan Zhang;Gautam Das

Orca-SR: A Real-Time Traffic Engineering Framework leveraging Similarity Joins

Orca-SR：利用相似性连接的实时流量工程框架

• 发表时间: 2020
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Augustine, Jees;Shetiya, Suraj;Asudeh, Azadeh;Thirumuruganathan, Saravanan;Nazi, Azade;Zhang, Nan;Das, Gautam;Srivastava, Divesh
• 通讯作者: Srivastava, Divesh