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Published in: Annals of Data Science 3/2022

30-03-2022

Factor Query Language (FQL): A Fundamental Language for the Next Generation of Intelligent Database

Authors: Xiangfu Meng, Jing Wen, Jiasheng Shi, Zihan Li, Jinxia Zhu, Peizhuang Wang

Published in: Annals of Data Science | Issue 3/2022

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Abstract

Factor space theory was first proposed in the 1980s with the rough set theory. After 30 years of development, factor space theory has established its completed theoretical architecture in mathematics; it has been proved very useful in causal analysis, intelligent reasoning and decision making. This paper proposes a Factor Query Language (FQL)—an SQL-like query language for operating the Factor Pedigree to make the factor space theory easily and widely used. First, the concepts associated with factor and factor space are presented. The factor pedigree which builds by knowledge increase and concept partitioning is presented; an XML specification–based storage method is also proposed for storing the factor pedigree. Next, the FQL (including FQL statements for insert, delete, update and select operations) is proposed. Two kinds of node encoding of factor pedigree strategies (interval-based encoding and prime + binary string-based encoding) are designed. They can facilitate the FQL query performance efficiently. The Factor Base Management System (FBMS) architecture and module functions are also presented.

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Metadata
Title
Factor Query Language (FQL): A Fundamental Language for the Next Generation of Intelligent Database
Authors
Xiangfu Meng
Jing Wen
Jiasheng Shi
Zihan Li
Jinxia Zhu
Peizhuang Wang
Publication date
30-03-2022
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science / Issue 3/2022
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-022-00391-y

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