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OPTIMIZING UNIVERSITY DATABASE MANAGEMENT THROUGH DYNAMIC FRAGMENTATION AND REALLOCATION: A CASE STUDY

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  • Additional Information
    • Publication Information:
      Academica Brancusi, 2024.
    • Publication Date:
      2024
    • Collection:
      LCC:Technology
      LCC:Mechanical engineering and machinery
    • Abstract:
      Dynamic allocation and reallocation of data fragments in distributed databases, particularly in the context of NoSQL databases, are essential for optimizing performance and resource utilization. This paper explores the application of dynamic fragment allocation and reallocation algorithms in a distributed database environment through a case study focused on university data management. By leveraging a well-defined database schema custom-made for university information management, the study highlights the integration of dynamic allocation strategies to efficiently manage faculties, departments, students, teachers, courses, and timetables. Through this case study, the paper demonstrates the significance of dynamic allocation and reallocation algorithms in enhancing data processing efficiency and optimizing resource utilization within distributed database systems.
    • File Description:
      electronic resource
    • ISSN:
      1844-640X
      2344-3669
    • Relation:
      https://www.utgjiu.ro/rev_mec/mecanica/pdf/2024-01/41_Adrian%20RUNCEANU,%20Mihaela-Ana%20RUNCEANU%20%20-%20OPTIMIZING%20UNIVERSITY%20DATABASE%20MANAGEMENT%20THROUGH%20DYNAMIC%20FRAGMENTATION%20AND%20REALLOCATION%20A%20CASE%20STUDY.pdf; https://doaj.org/toc/1844-640X; https://doaj.org/toc/2344-3669
    • Accession Number:
      edsdoj.0a99156cce4cfabc1a984ceff18c3c