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III: Small: NeuroDB: A Neural Network Framework for Efficiently Answering Database Queries Approximately

III：小：NeuroDB：一种高效回答数据库查询的神经网络框架

基本信息

批准号：
2128661
负责人：
Cyrus Shahabi
金额：
$ 50万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2128661&HistoricalAwards=false
关键词：
III Small NeuroDB Neural Network

项目摘要

We are witnessing the age of data explosion. Large amounts of data are continuously generated by people, sensors and computers and then stored in computer systems. Many modern applications require these systems answer questions about the data quickly, accurately and with low storage costs. This is a challenging task, because increasing speed comes at the expense of accuracy, and there is a trade-off between the three goals (speed, storage overhead and accuracy). The traditional approach to address these issues was to develop customized computer algorithms, each designed to answer a specific type of question (called query type), by striking a compromise between these goals. Such a process has proven to be time consuming and needs to be repeated for each query type. The ultimate goal is to design a system that can automatically search among possible algorithms to find the ones with the best time/space/accuracy trade-offs for each query type. This project takes a first step in this direction by utilizing Artificial Intelligence (AI) to replace all customized algorithms for different query types with a single neural network system (similar to network of neurons in human brain) that can answer any query type after seeing enough examples of questions and answers. Such an approach will significantly reduce (or eliminate) human intervention in designing/choosing algorithms for different query types, while improving upon the time/space/accuracy trade-offs for answering them. This will benefit a broad range of application domains, especially given the data-driven nature of most of today's applications. For example, this approach will greatly benefit data-driven areas, such as smart-cities, transportation, energy, environment and health. Finally, this project will also be extremely attractive to diverse students (both undergraduates and graduates) who are these days more attracted to AI, and will recruit them to contribute to the field of data management.The main thesis of this project is that queries can be represented by functions which can be approximated. Thus, a generic neural network framework will be developed, dubbed NeuroDB, that can learn a model to approximate the query function. The learned model can then be used to find approximate answers to any type of query. The initial focus of NeuroDB will be on distance-to-nearest-neighbor queries and range aggregate queries, two important building blocks of many real-world applications. Preliminary results show that for these two query types, NeuroDB is orders of magnitude faster compared to state-of-the-art hand-crafted algorithms, and only uses a fraction of the data size for storage, while providing similar accuracy (the model concisely learns query and data distributions). NeuroDB will then be extended to a generic framework that can answer a broader set of query types, thus replacing different database operations with neural networks. The challenges in doing so are twofold. First, designing a neural network even for answering a specific query type is challenging. It requires a deeper understanding, both theoretically and empirically, of the approximation power of neural networks, and how it changes with network architecture and training process, for the specific query type. Second, the observations about specific query types need to be generalized to design a framework that can answer other query types, and a learning methodology is needed that is able to train accurate models for answering them. NeuroDB will be developed as an open-source software system to be used and extended by the research community.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.

我们正在见证数据爆炸的时代。大量的数据由人、传感器和计算机不断产生，然后存储在计算机系统中。许多现代应用要求这些系统快速、准确地回答有关数据的问题，并且存储成本低。这是一项具有挑战性的任务，因为提高速度是以牺牲准确性为代价的，并且在三个目标（速度，存储开销和准确性）之间存在权衡。解决这些问题的传统方法是开发定制的计算机算法，每个算法都旨在通过在这些目标之间达成妥协来回答特定类型的问题（称为查询类型）。事实证明，这样的过程非常耗时，并且需要针对每种查询类型重复进行。最终目标是设计一个系统，可以自动搜索可能的算法，找到最好的时间/空间/准确性权衡每种查询类型。该项目朝着这个方向迈出了第一步，利用人工智能（AI）将不同查询类型的所有定制算法替换为单个神经网络系统（类似于人脑中的神经元网络），该系统可以在看到足够的问题和答案示例后回答任何查询类型。这样的方法将显著减少（或消除）在针对不同查询类型设计/选择算法时的人为干预，同时改进用于回答它们的时间/空间/准确性权衡。这将使广泛的应用领域受益，特别是考虑到当今大多数应用程序的数据驱动特性。例如，这种方法将大大有利于数据驱动的领域，如智能城市，交通，能源，环境和健康。最后，这个项目也将非常吸引不同的学生（包括本科生和研究生），他们现在更喜欢人工智能，并将招募他们为数据管理领域做出贡献。这个项目的主要论点是查询可以由可以近似的函数表示。因此，将开发一个通用的神经网络框架，称为NeuroDB，它可以学习一个模型来近似查询函数。然后，学习的模型可以用于找到任何类型查询的近似答案。NeuroDB最初的重点将是距离到最近邻查询和范围聚合查询，这是许多现实世界应用程序的两个重要构建块。初步结果表明，对于这两种查询类型，NeuroDB比最先进的手工算法快了几个数量级，并且只使用了一小部分数据大小进行存储，同时提供了类似的准确性（模型简明地学习查询和数据分布）。然后，NeuroDB将扩展为一个通用框架，可以回答更广泛的查询类型，从而用神经网络取代不同的数据库操作。这样做的挑战是双重的。首先，设计一个神经网络，即使是回答特定的查询类型也是具有挑战性的。它需要从理论和经验上更深入地理解神经网络的近似能力，以及它如何随着网络架构和训练过程的变化而变化。其次，需要对特定查询类型的观察进行概括，以设计一个可以回答其他查询类型的框架，并且需要一种能够训练准确模型来回答它们的学习方法。NeuroDB将被开发为一个开源软件系统，供研究界使用和扩展。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。