We overview recent progress on the longstanding problem of incremental view maintenance (IVM), with a focus on the fine-grained complexity and optimality of IVM for classes of conjunctive queries. This theoretical progress guided the development of IVM engines that reported practical benefits in academic papers and industrial settings. When taken in isolation, each of the reported advancements is but a small increment. Yet when taken together, they may well pave the way to a deeper understanding of the IVM problem. This paper accompanies the invited Gems of PODS 2024 talk with the same title. Some of the works highlighted in this paper are based on prior or on-going collaborations with: Ahmet Kara, Milos Nikolic, and Haozhe Zhang in the F-IVM project; and Mahmoud Abo Khamis, Niko G\"obel, Hung Ngo, and Dan Suciu at RelationalAI.

翻译：本文综述了增量视图维护问题的近期研究进展，重点关注了合取查询类增量视图维护的细粒度复杂性与最优性问题。这些理论进展推动了增量视图维护引擎的发展，并在学术论文与工业场景中展现了实际效益。若将每项进展孤立看待，它们仅是微小增量；但综观全局，这些突破性进展很可能为深化对增量视图维护问题的理解铺平道路。本文系为PODS 2024特邀报告《近期增量视图维护研究进展》撰写的配套论文。文中提及的部分工作基于与Ahmet Kara、Milos Nikolic、张浩哲在F-IVM项目中的现有/持续合作，以及与Mahmoud Abo Khamis、Niko Göbel、Hung Ngo、Dan Suciu在RelationalAI公司的协作成果。