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.

我们目睹了数据爆炸的年龄。大量数据是由人员，传感器和计算机不断生成的，然后存储在计算机系统中。许多现代应用都需要这些系统快速，准确和较低的存储成本回答有关数据的问题。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）之间进行了权衡。解决这些问题的传统方法是开发自定义的计算机算法，每种算法旨在通过在这些目标之间构成妥协来回答特定类型的问题（称为查询类型）。事实证明，这样的过程很耗时，并且需要重复每种查询类型。最终目标是设计一个可以在可能的算法之间自动搜索的系统，以找到每种查询类型具有最佳时间/空间/准确性权衡折衷的系统。该项目通过利用人工智能（AI）来朝着这一方向迈出第一步，以单个神经网络系统（类似于人脑中的神经元网络类似）来代替不同查询类型的所有自定义算法，这些算法可以在看到足够的问题和答案示例后回答任何查询类型。这种方法将显着减少（或消除）人类干预，以设计/选择不同查询类型的算法，同时改善回答它们的时间/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性质。例如，这种方法将极大地受益于数据驱动的领域，例如智能城市，运输，能源，环境和健康。最后，该项目还将对这些天更吸引AI的多元化学生（包括本科生和毕业生）非常有吸引力，并将招募他们为数据管理领域做出贡献。该项目的主要论点是，查询可以通过可以近似的功能来表示。因此，将开发一个名为NeuroDB的通用神经网络框架，该框架可以学习一个模型以近似查询函数。然后可以使用学习的模型来找到任何类型的查询的近似答案。 NeuroDB的最初焦点将放在距离最新的邻居查询和范围骨料查询上，这是许多现实世界应用的两个重要构件。初步结果表明，对于这两种查询类型，NeuroDB的数量级比最新的手工制作的算法要快，并且仅使用数据大小的一小部分进行存储，同时提供了相似的准确性（该模型简洁地学习查询和数据分布）。然后，NeuroDB将扩展到一个通用框架，该框架可以回答更广泛的查询类型，从而用神经网络替换不同的数据库操作。这样做的挑战是双重的。首先，设计神经网络即使回答特定查询类型也很具有挑战性。在理论上和经验上，它需要更深入的了解神经网络的近似能力，以及针对特定查询类型的网络体系结构和培训过程的变化。其次，需要对有关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，并且需要一种学习方法，以便能够训练准确的模型以回答它们。 NeuroDB将作为一种开源软件系统开发，该软件系统将被研究社区使用和扩展。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。