Elements: Software: Towards Efficient Embedded Data Processing

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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.