Elements: Software: Towards Efficient Embedded Data Processing

要素：软件：实现高效的嵌入式数据处理

• 批准号: 2407755
• 负责人: Jignesh Patel
• 金额: $ 59.98万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2407755&HistoricalAwards=false
• 关键词: Elements; Software; Towards; Efficient; Embedded

Embedded databases are ubiquitous, though that fact may not be widely-recognized. Every smart phone has many embedded databases, which implies that billions of people worldwide carry dozens of databases in their phones/pockets every day. Many of these databases are powered by data processing technology that has not kept up with the pace with which the underling hardware in phones have evolved. As a result, data processing is slow, and consumes more energy than needed. The focus of this proposal is on developing new data processing technology for mobile devices that targets a 10X efficiency and performance improvements. The aims of the project go beyond more efficient data processing on phones to also include more efficient processing in embedded environments, which also includes databases running on laptops. Thus, the project aims for a broad impact on database across a spectrum of mobile devices.The technical contributions of this project are in recognizing that modern hardware, even at the ?low-end? which includes mobile phones and laptops, now have multiple processing cores, relatively large amounts of memory, and flash storage. There is a critical need for a new class of embedded data processing systems that can work efficiently, and effectively on such modern mobile platforms. This project aims to build a system, called Hustle, to address this need. The project will design, develop and implement a range of data processing methods, which include predicate-based concurrency control mechanisms, query processing methods that inherently expose and exploit opportunities for intra and inter-operator parallelism, and query optimization methods that target embedded settings.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.

嵌入式数据库无处不在，尽管这一事实可能没有得到广泛认可。每一部智能手机都有许多嵌入式数据库，这意味着全世界数十亿人每天都在他们的手机/口袋里携带着几十个数据库。这些数据库中的许多都是由数据处理技术驱动的，但这些技术跟不上手机底层硬件的发展步伐。因此，数据处理速度很慢，并且消耗的能量比需要的要多。该提案的重点是为移动设备开发新的数据处理技术，目标是提高10倍的效率和性能。该项目的目标不仅仅是在手机上更有效地处理数据，还包括在嵌入式环境中更有效地处理数据，其中也包括在笔记本电脑上运行的数据库。因此，该项目的目标是在移动设备范围内对数据库产生广泛的影响。这个项目的技术贡献在于认识到现代硬件，即使是在低端？包括手机和笔记本电脑，现在有多个处理核心，相对较大的内存和闪存。迫切需要一种新型的嵌入式数据处理系统，这种系统可以在这样的现代移动平台上高效地工作。该项目旨在建立一个名为Hustle的系统来解决这一需求。该项目将设计、开发和实现一系列数据处理方法，其中包括基于谓词的并发控制机制、固有地暴露和利用操作符内部和操作符间并行性机会的查询处理方法，以及针对嵌入式设置的查询优化方法。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

Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis

简单自适应查询处理与学习查询优化器：观察和分析

• DOI: 10.14778/3611479.3611501
• 发表时间: 2023
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Zhang, Yunjia;Chronis, Yannis;Patel, Jignesh M.;Rekatsinas, Theodoros
• 通讯作者: Rekatsinas, Theodoros