Reengineering Probabilistic Relational Databases with Fuzzy Probability Measures into XML Model-Reference-Cited by-同舟云学术

Reengineering Probabilistic Relational Databases with Fuzzy Probability Measures into XML Model

Published:2017-07 Issue:3 Volume:28 Page:26-47
ISSN:1063-8016
Container-title:Journal of Database Management
language:en
Short-container-title:

Author:

Ma Zongmin¹,Li Chengwei²,Yan Li²

Affiliation:

1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China

2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Abstract

This paper concentrates on modeling probabilistic events with fuzzy probability measures in relational databases and XML (Extensible Markup Language). Instead of crisp probability degrees or interval probability degrees, fuzzy sets are applied to represent imprecise probability degrees in relational databases and XML. A probabilistic XML model with fuzzy probability measures is introduced, which incorporates fuzzy probability measures to handle imprecision and uncertainty. In particular, the formal approach to reengineering the relational database model with fuzzy probability measures into the DTD (document type definition) model with fuzzy probability measures is developed in the paper.

Publisher

IGI Global

Subject

Hardware and Architecture,Information Systems,Software

Reference47 articles.

1. On the Connections between Relational and XML Probabilistic Data Models

2. A note on probability/possibility consistency for fuzzy events.;J. M.Baldwin;Proceedings of the 6th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems,1996

3. The management of probabilistic data

4. A fuzzy representation of data for relational databases

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Data modeling and querying with fuzzy sets: A systematic survey;Fuzzy Sets and Systems;2022-09

2. Monitor and Detect Suspicious Transactions With Database Forensic Analysis;Digital Forensics and Forensic Investigations;2020

3. Multi-Fuzzy-Objective Graph Pattern Matching with Big Graph Data;Journal of Database Management;2019-10

4. Multi-Objective Optimization-Based Networked Multi-Label Active Learning;Journal of Database Management;2019-04

5. Modeling Probabilistic Data with Fuzzy Probability Measures in UML Class Diagrams;Advances in Intelligent Systems and Computing;2019