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科学、技術、工学、医学(STEM)分野に焦点を当てています | ISSN: 2995-8067  G o o g l e  Scholar

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IgMin Research | マルチディシプリナリーオープンアクセスジャーナルは、科学、技術、工学、医学(STEM)の広範な分野における研究と知識の進展に貢献することを目的とした権威ある多分野のジャーナルです.

Engineering

Data Modeling at IgMin Research | Engineering Group

私たちの使命は、学際的な対話を促進し、広範な科学領域にわたる知識の進展を加速することです.

について

Data modeling is a crucial aspect of modern scientific inquiry, technological advancements, engineering solutions, and medical research. It involves the creation of abstract representations of real-world systems, phenomena, or processes, enabling a deeper understanding, analysis, and prediction of complex interactions. IgMin Research presents a multidisciplinary exploration of Data Modeling, delving into its various applications across diverse domains.

In today's data-driven landscape, effective data modeling enhances decision-making, facilitates accurate simulations, and supports innovation. Our journal's Data Modeling section invites researchers, practitioners, and enthusiasts to contribute and explore the intricacies of this field. From conceptual design to implementation, our platform serves as a hub for exchanging knowledge, methodologies, and breakthroughs.

  • Conceptual data modeling
  • Logical and physical data modeling
  • Entity-relationship modeling
  • Object-oriented data modeling
  • Semantic data modeling
  • Data modeling languages (UML
  • ERD
  • RDF
  • etc.)
  • Data integration and interoperability
  • Big data modeling and analytics
  • Geospatial data modeling
  • Temporal data modeling
  • Multi-dimensional data modeling
  • Data modeling for machine learning
  • Data modeling for artificial intelligence
  • Database design and optimization
  • NoSQL data modeling
  • Data warehousing and data lakes
  • Ontology development and modeling
  • Data modeling for scientific simulations
  • Biomolecular and genetic data modeling
  • Clinical data modeling in healthcare
  • Environmental modeling and data representation
  • Social network analysis and modeling
  • Business process modeling and analysis
  • Data modeling for cybersecurity
  • Ethical considerations in data modeling