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Data Mining the Yeast Genome in a Lazy Functional Language

机译:惰性功能语言的酵母基因组数据挖掘

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Critics of lazy functional languages contend that the languages are only suitable for toy problems and are not used for real systems. We present an application (PolyFARM) for distributed data mining in relational bioinformatics data, written in the lazy functional language Haskell. We describe the problem we wished to solve, the reasons we chose Haskell and relate our experiences. Laziness did cause many problems in controlling heap space usage, but these were solved by a variety of methods. The many advantages of writing software in Haskell outweighed these problems. These included clear expression of algorithms, good support for data structures, abstraction, modularity and generalisation leading to fast prototyping and code reuse, parsing tools, profiling tools, language features such as strong typing and referential transparency, and the support of an enthusiastic Haskell community.
机译:惰性功能语言的批评者认为,这些语言仅适用于玩具问题,不适用于实际系统。我们提出了一种应用程序(PolyFARM),用于以惰性函数语言Haskell编写的相关生物信息学数据中的分布式数据挖掘。我们描述了我们希望解决的问题,选择Haskell的原因并结合了我们的经验。懒惰确实在控制堆空间使用方面引起了许多问题,但是这些问题可以通过多种方法来解决。用Haskell编写软件的许多优点胜过这些问题。这些措施包括清晰的算法表达,对数据结构的良好支持,抽象,模块化和泛化,从而实现快速的原型设计和代码重用,解析工具,性能分析工具,语言功能(如强大的键入和引用透明性)以及热情的Haskell社区的支持。

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