IEEE/ACM Trans Comput Biol Bioinform. 2018 Sep-Oct;15(5):1692-1705. doi: 10.1109/TCBB.2017.2743706. Epub 2017 Aug 24.
The vast majority of phylogenetic databases do not support declarative querying using which their contents can be flexibly and conveniently accessed and the template based query interfaces they support do not allow arbitrary speculative queries. They therefore also do not support query optimization leveraging unique phylogeny properties. While a small number of graph query languages such as XQuery, Cypher, and GraphQL exist for computer savvy users, most are too general and complex to be useful for biologists, and too inefficient for large phylogeny querying. In this paper, we discuss a recently introduced visual query language, called PhyQL, that leverages phylogeny specific properties to support essential and powerful constructs for a large class of phylogentic queries. We develop a range of pruning aids, and propose a substantial set of query optimization strategies using these aids suitable for large phylogeny querying. A hybrid optimization technique that exploits a set of indices and "graphlet" partitioning is discussed. A "fail soonest" strategy is used to avoid hopeless processing and is shown to produce dividends. Possible novel optimization techniques yet to be explored are also discussed.
绝大多数系统发育数据库不支持使用声明式查询来灵活方便地访问其内容，而且它们支持的基于模板的查询接口不允许任意推测性查询。因此，它们也不支持利用独特的系统发育属性进行查询优化。虽然有少数图形查询语言，如 XQuery、Cypher 和 GraphQL，供有计算机知识的用户使用，但大多数语言过于通用和复杂，对生物学家来说用处不大，而且对于大型系统发育查询来说效率也太低。在本文中，我们讨论了一种最近引入的可视化查询语言 PhyQL，它利用系统发育特有的属性来支持一大类系统发育查询的基本和强大的结构。我们开发了一系列修剪辅助工具，并提出了一系列使用这些辅助工具的查询优化策略，适用于大型系统发育查询。讨论了一种利用索引和“图元”分区的混合优化技术。使用“尽快失败”策略来避免无望的处理，并证明了它的好处。还讨论了可能尚未探索的新的优化技术。
