通过深度学习实现灵活的算子嵌入
Flexible Operator Embeddings via Deep Learning
摘要：
将机器学习集成到数据库管理系统的内部需要大量的特征工程，这是一个人工密集型过程，用于确定表示与任务相关的信息的最佳方式。除了劳动密集型之外，手工设计特征的过程通常必须对每个数据管理任务重复，并且可能对底层数据库做出并非普遍正确的假设。我们引入了灵活的运算符嵌入，这是一种深度学习技术，用于自动将查询运算符转换为对多个数据管理任务有用的特征向量，并为底层数据库定制。我们的方法的工作原理是利用运营商的上下文，从而产生一个神经网络，快速转换稀疏表示的查询运营商到密集的，信息丰富的特征向量。在实验中，我们表明，我们的灵活的操作符嵌入在许多数据管理任务中表现良好，使用合成和真实世界的数据集。
英文摘要：
Integrating machine learning into the internals of database management systems requires significant feature engineering, a human effort-intensive process to determine the best way to represent the pieces of information that are relevant to a task. In addition to being labor intensive, the process of hand-engineering features must generally be repeated for each data management task, and may make assumptions about the underlying database that are not universally true. We introduce flexible operator embeddings, a deep learning technique for automatically transforming query operators into feature vectors that are useful for a multiple data management tasks and is custom-tailored to the underlying database. Our approach works by taking advantage of an operator's context, resulting in a neural network that quickly transforms sparse representations of query operators into dense, information-rich feature vectors. Experimentally, we show that our flexible operator embeddings perform well across a number of data management tasks, using both synthetic and real-world datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
