A fuzzy database engine for mongoDB
Metadata
Show full item recordEditorial
Wiley
Materia
Fuzzy databases Fuzzy NoSQL databases mongoDB
Date
2022-01-11Referencia bibliográfica
Medina, JM, Blanco, IJ, Pons, O. A fuzzy database engine for mongoDB. Int J Intell Syst. 2022; 1- 34. doi:[10.1002/int.22807]
Sponsorship
Universidad de Granada; MCIN/AEI/10.13039/501100011033; FEDER: “Una manera de hacer Europa” PGC2018‐096156‐B‐I00Abstract
Big Data are a paradigm through which valuable information
is achieved through the analysis of a large
amount of data. The sources of these data can be varied,
from data streams that will be processed in real
time, to the exploitation of transactional data stored in
databases. For this last use, due to their scalability, the
NoSQL databases, like mongoDB, a DBMS oriented to
documents, have been consolidated as a powerful tool
for the storage and processing of large volumes of
data. On the other hand, information sources for Big
Data algorithms can contain imprecise information,
and the way to obtain, aggregate and present results
can have an imprecise nature as well. For this reason, it
is useful to provide fuzzy extensions to these DBMSs.
In the case of MongoDB, there are few proposals and
not very complete. This paper describes fzMongoDB,
a fuzzy database engine that provides the mongoDB
database with the capacity to store documents with
imprecise information and to retrieve them in a flexible
way. It is implemented and integrated on the mongoDB
server using the resources it provides. The model
and implementation of fzMongoDB also includes an
indexing mechanism that accelerates the retrieval
process on fuzzy queries. Also, the performance of
these indexing mechanisms is evaluated.