转换数据字时必须记住的事项
What You Must Remember When Transforming Datawords
摘要：
流数据字符串转换器（SDST）被引入来建模一类命令式程序和一类函数式程序，操纵数据项列表。这些可以用来编写常用的例程，如插入，删除和反转。SDST可以处理来自潜在无限数据域的数据值。流串转换器（SST）的模型是SDST的片段，其中无限的数据域被丢弃，只考虑有限的字母表。从语言理论的角度对SST进行了大量的研究。我们把数据引入到SST中，就像把数据引入到有限状态自动机中得到寄存器自动机一样。其结果是流串寄存器传感器（SSRT），这是SDST的子类。SDST可以在数据域上使用线性顺序比较数据值，这是SSRT无法完成的。我们给出了一个机器独立的SSRTs与起源语义的表征，沿着Myhill-Nerode定理。相似模型的独立于机器的特征描述已经形成了学习算法的基础，并使我们能够理解模型的片段。换能器的原点语义跟踪输出的哪些位置源自输入的哪些位置。虽然是一个限制，但使用原点语义是合理的，并且已知可以简化与换能器相关的许多问题。除了确定性寄存器自动机的特征之外，我们还使用起源语义作为技术构建块。然而，我们需要在这些基础上进行更多的建设，以克服SSRT特有的一些挑战。
英文摘要：
Streaming Data String Transducers (SDSTs) were introduced to model a class of imperative and a class of functional programs, manipulating lists of data items. These can be used to write commonly used routines such as insert, delete and reverse. SDSTs can handle data values from a potentially infinite data domain. The model of Streaming String Transducers (SSTs) is the fragment of SDSTs where the infinite data domain is dropped and only finite alphabets are considered. SSTs have been much studied from a language theoretical point of view. We introduce data back into SSTs, just like data was introduced to finite state automata to get register automata. The result is Streaming String Register Transducers (SSRTs), which is a subclass of SDSTs. SDSTs can compare data values using a linear order on the data domain, which can not be done by SSRTs. We give a machine independent characterization of SSRTs with origin semantics, along the lines of Myhill-Nerode theorem. Machine independent characterizations for similar models have formed the basis of learning algorithms and enabled us to understand fragments of the models. Origin semantics of transducers track which positions of the output originate from which positions of the input. Although a restriction, using origin semantics is well justified and known to simplify many problems related to transducers. We use origin semantics as a technical building block, in addition to characterizations of deterministic register automata. However, we need to build more on top of these to overcome some challenges unique to SSRTs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
