论文标题
多目标优化：基本方法，并通过灵活的天际线查询超越它们
Multi-objective optimization: basic approaches and moving beyond them through flexible skyline queries
论文作者
论文摘要
涉及几个（可能相互矛盾）标准的同时优化的科学研究领域被称为多目标优化。从大型数据集中有效过滤和提取有趣数据的能力是现代数据库系统中的关键任务之一。本文概述了用于处理数据库领域问题的最常见方法，并描述了一个名为Flexible Skylines的新型框架。在分析了单一和多优化问题之间的主要差异之后，我将讨论用于处理多优选问题的三种主要基本方法：词典方法，Top-K查询和Skylines。将讨论每种方法，分析专业人士，适用性范围和主要问题，这激发了引入灵活的Skylines创新框架的需求。对这种方法的综述将表明其在基本方法方面的优势，以及克服大多数弊端的能力。
The area of scientific research that deals with the simultaneous optimization of several (possibly conflicting) criteria is named multi-objective optimization. The ability to efficiently filter and extract interesting data out of large datasets is one of the key tasks in modern database systems. This paper provides a general overview of the most common approaches employed to handle the problem in the field of databases, and describes a novel framework named flexible skylines. After analyzing the main differences between single and multi-optimization problems, I will discuss the three main basic approaches used to handle multi-optimization problems: lexicographic approach, top-k queries and skylines. Each methodology will be discussed, analyzing the pros, the range of applicability and the main issues, which motivate the need to introduce the flexible skylines innovative framework. A review of this approach will show its superiority with respect to the basic approaches, as well as the capability to overcome the majority of their drawbacks.
