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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Optimizing Phylogenetic Queries for Performance
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Optimizing Phylogenetic Queries for Performance

机译：优化系统发育查询以提高性能

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摘要

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.
机译：绝大多数系统发育数据库都不支持声明式查询，使用该声明式查询可以灵活方便地访问其内容，并且它们所支持的基于模板的查询接口不允许进行任意推测性查询。因此，它们也不支持利用独特的系统发育特性进行查询优化。虽然对于计算机精明的用户而言，存在少量的图形查询语言，例如XQuery，Cypher和GraphQL，但大多数语言过于笼统和复杂，无法供生物学家使用，而对于大型系统发育查询而言，效率也很低。在本文中，我们讨论了一种最新引入的可视化查询语言PhyQL，该语言利用系统发育特定的属性来支持针对大量系统发育查询的重要且功能强大的构造。我们开发了一系列修剪辅助工具，并提出了使用这些辅助工具的大量查询优化策略，这些策略适用于大型系统发育查询。讨论了利用一组索引和“小图”划分的混合优化技术。 “尽快失败”策略用于避免无望的处理，并显示出可带来的好处。还讨论了可能要探索的新颖优化技术。

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