Fuzzy request handler for Mongo QL derived from SQL

Abstract

Relational DBMS are often used to store fuzzy values, but problems arise with putting such data in a tabular form. Moreover, there appears a problem of storing both the crisp and fuzzy data related to one subject domain in one column of a relational table. This article considers the mechanism of storing crisp and fuzzy values and linguistic variables in the document-oriented Mongo DBMS. The data are stored in the collection as GeoJSON geometry; different geometries are used for different data options. The possibility of storing crisp scalar values, crisp value sets, crisp value intervals and fuzzy values in the collection documents is described. For data processing by means of SQL queries, the context-free grammar of the SQL subset is described, according to which lexer and parser are generated. In order to form the structure of an abstract syntactic tree, a corresponding object model has been implemented. A translator application has been developed, which allows converting SQL queries related to the crisp and fuzzy data into Mongo QL queries. The algorithm of fuzzy queries translation process is suggested; the geometrical interpretation of data comparison operations is described. The examples show the options of fuzzy comparison operations for different value options.

A fuzzy database engine for mongoDB

“…Our fzMongoDB module beats other proposals available in the literature like 19,20,22 which do not allow one to represent fuzzy data into the database. Although the proposal in Reference [21] allows one to represent linguistic labels into , it uses an inefficient method to query them. Besides, all of them use an external module to translate the fuzzy statements into the statements processable by the database.…”

“…The problem is the lack of explanation on how the system performs the necessary transformations from FMQL to F‐XML and how executes this translated code. Astachova et al [21] present a proposal based on the implementation of a parser that translates fuzzy queries, expressed by means of an extension of SQL, to an expression on Mongo query language to be executed on it. The database stores collections that can contain fuzzy data and linguistic values using a geometry representation based on the use of GeoJSON type.…”