Towards an integrated rough set and data modelling framework for data management and knowledge extraction

Salem Chakhar, Zouhaier Brahmia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Data models and database systems are excellent tools to store and manage data. However, most of available data models and database systems lack effective techniques to extract relevant knowledge form raw data. Combining data modelling approaches and machine learning techniques represent a promising road to design and develop integrated data management and knowledge extraction systems. In this paper, first we propose a Rough Semantic data Model (RSM) based on a coupling between semantic data modelling and rough sets concepts. Then, we introduce the design of a framework that supports RSM and provides data management and knowledge extraction functionalities.
Original languageEnglish
Title of host publicationArtificial Intelligence and Smart Environment ICAISE’2022
Subtitle of host publicationProceedings of the 4th International Conference on Artificial Intelligence and Smart Environment
EditorsYousef Farhaoui, Alvaro Rocha, Zouhaier Brahmia, Bharat Bhushab
PublisherSpringer
Pages800-805
Number of pages6
ISBN (Electronic)9783031262548
ISBN (Print)9783031262531
DOIs
Publication statusPublished - 8 Mar 2023
EventThe 4th International Conference on Artificial Intelligence and Smart Environments - Errachidia, Morocco
Duration: 24 Nov 202226 Nov 2022
https://bdsde.sciencesconf.org/

Publication series

NameLecture Notes on Networks and Systems
PublisherSpringer
Volume635
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceThe 4th International Conference on Artificial Intelligence and Smart Environments
Abbreviated titleICAISE 2022:
Country/TerritoryMorocco
CityErrachidia
Period24/11/2226/11/22
Internet address

Keywords

  • database
  • rough set theory
  • data model
  • knowledge extraction

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