IMPLEMENTATION OF DFA PARSER FOR MANUFACTURING QUERY LANGUAGE TOKENS

Installing a manufacturing method might be very expensive and time consuming project. Organization should examine and decide on how best to make this decision of selecting appropriate process meeting their requirements. In order to improve the manufacturing cycle more than 110 manufacturing processes have been proposed. The objectives aimed at and the functions focused on by these processes vary. The process should be flexible enough to accommodate reasonable changes in design. This poses a great challenge to a manager in selection of effective and economical manufacturing process. Different organizations have different objectives and based on their specific requirement they deploy suitable process conforming to their objective. Today's business scenario is highly competitive, complex and dynamic in nature which demands strategic planning meeting the challenges of changing time. Recently, we have developed a tool to enable the end user a quick selection of appropriate manufacturing method based on multiple objectives. The information pertaining to the method selection is stored in a persistent Relational DataBase Management System (RDBMS) which can be manipulated by the end user as the organizational objectives and the market needs change. The end user instead of querying the database directly will use the natural language, termed as Manufacturing Query Language (MQL) designed by us, which is interfaced with RDBMS using prolog. To implement MQL, we have defined a finite set of symbols, words and language rules, MQL grammar. In this paper we present a deterministic finite automata (DFA) parser developed by us for parsing MQL tokens. The state table and state diagrams are developed for different tokens of MQL identified by us. State information is stored in a persistent database management system as a measure towards improving efficiency and extensibility. Currently, MQL consists of only few commands and more commands will be added to MQL in near future.