A customised automata algorithm and toolkit for language learning and application. (2018)

• Record Type: Journal Article
• Authors: Wang, Ruoyu; Li, Guoqiang; Xiang, Jianwen; Cai, Hongming
• Is Part Of: International journal of big data intelligence. Volume 5:Number 1/2(2018)
• Page Start: 114
• Page End: 123
• Publication Date: 2018
• Subjects: automata -- customise -- C++ -- big data analytics -- semantics -- toolkit -- L* -- automata theory -- DFA -- NFA -- PDA -- regular language -- context free language -- Infernet -- framework; Big data -- Periodicals; 005.705
• Languages: English
• ISSNs: 2053-1389

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.