Title: A customised automata algorithm and toolkit for language learning and application

Authors: Ruoyu Wang; Guoqiang Li; Jianwen Xiang; Hongming Cai

Addresses: School of Software, Shanghai Jiao Tong University, Shanghai, China ' School of Software, Shanghai Jiao Tong University, Shanghai, China ' Wuhan University of Technology, Hubei, China ' School of Software, Shanghai Jiao Tong University, Shanghai, China

Abstract: Automata are abstract computing machines. They play a basic role in computability theory and programming language theory. More recently in data analytics, data automata have become a formal way to represent pipelines and workflows. However, in researches involved with automata, there are still situations where redundant work and ununified standards occur. In order to solve that problem, we propose a new toolkit: CAT, which provides a simple and unified framework for automaton construction and customisation. We adopted both structural and behavioural analysis in order to design the body structure. Several calculus algorithms are implemented according to the theoretical accomplishments and designed as overloaded operators. To test the correctness and performance of this toolkit, several bare automata were constructed and compared with 'GREP' in Ubuntu Linux. The result showed that CAT has realised most of the design purposes and presents a more illustrative way for writing codes of automata construction and calculation.

Keywords: automata; customise; C++; big data analytics; semantics; toolkit; L*; automata theory; DFA; NFA; PDA; regular language; context free language; Infer.net; framework.

DOI: 10.1504/IJBDI.2018.088285

International Journal of Big Data Intelligence, 2018 Vol.5 No.1/2, pp.114 - 123

Received: 02 May 2016
Accepted: 06 Dec 2016

Published online: 01 Dec 2017 *

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