View materialization, index selection, and plan caching are well-known techniques for optimization of query processing in database systems. The essence of these tasks is to select and save a subset of the most useful candidates (views/indexes/plans) for reuse within given space/time budget constraints. In this paper, based on the View Selection Problem, we propose a unified view on these problems. We identify the root causes of the complexity of these selection problems and provide a detailed analysis of techniques to cope with them. Our survey provides a modern classification of selection algorithms known in the literature, including the latest ones based on Machine Learning. We provide a ground for the reuse of the selection techniques between different optimization scenarios and highlight challenges and promising directions in the field.

翻译：视图物化、索引选择与计划缓存是数据库系统中查询处理优化的经典技术。这些任务的核心在于：在给定的空间/时间预算约束下，选择并保存一组最有用的候选对象（视图/索引/计划）以供复用。本文基于视图选择问题，提出了一个针对此类问题的统一框架。我们识别了这些选择问题复杂性的根本原因，并对应对这些复杂性的技术进行了详细分析。本综述对文献中已知的选择算法（包括基于机器学习的最新算法）进行了现代分类。我们为不同优化场景间选择技术的复用提供了基础，并指出了该领域的挑战与有前景的研究方向。