The Methodology Of Database Design In Organization Managemen

The methodology of database design in organization management systems To cite this article: I L Chudinov et al 2017 J. Phys.: Conf. Ser

The paper describes a unified methodology of database design tailored for management information systems. It emphasizes that designing the conceptual information model of the domain area is both critical and labor-intensive within the overall database development process. The approach integrates principles for developing relational databases with a focus on the user’s informational needs. The methodology outlines a three-stage process for creating the conceptual information model, which is central to effective database design, particularly in small and medium-sized enterprise contexts where specialized information systems are essential for automating and expanding business processes.

The paper discusses how management information systems, integral to enterprise operations across various functions like manufacturing, HR, content management, and customer relations, rely on structured databases. These systems often require reengineering within broader enterprise management systems, necessitating a methodical approach to database design. The authors adopt the ANSI/SPARC three-level architecture—comprising conceptual, logical, and physical schemas—as a framework for the design process, with particular focus on the initial conceptual modeling stage.

The methodology proposed emphasizes an incremental, sequential approach to designing the conceptual model, accommodating both current and anticipated user information needs. It involves analyzing user requirements, formalizing them into initial entities, normalizing attributes, and establishing relationships between entities. This process includes carefully identifying attributes, their domains, and multiple values, as well as classifying data to determine whether encoding is necessary to optimize storage and processing efficiency.

A significant aspect of the methodology is its focus on iterative refinement. It supports the formalization of user needs through structured representations, facilitating integration of new or changing requirements over time without compromising existing schemas. The process also emphasizes collaboration between information requirement suppliers and analysts, ensuring that the model accurately reflects organizational realities and user expectations.

In the initial phase, the method entails semantic analysis of the domain, identifying attributes from various information formats such as documents, user requests, and files. Attributes are then systematically described, overlapping domains assessed for compatibility, and methods for normalization and attribute compression applied to streamline data schemas. The criteria for encoding attributes—particularly those with large dictionaries of possible values—are formalized to determine when classifier encoding is advantageous, improving storage efficiency.

The subsequent stage involves relating the normalized entities within a conceptual model, establishing hierarchies and relationships using algorithms that consider hierarchy relationships, join relationships, and transitive associations. This ensures a coherent, consistent structure that accurately models organizational objects and their interactions.

The final stage consolidates the devised entities and relationships into a comprehensive conceptual information model. This model serves as a foundation for subsequent physical database implementation, supporting scalability and adaptability for evolving organizational needs.

Overall, the proposed methodology presents a formalized, flexible framework adaptable to various application areas of organizational management. It emphasizes a formal description of current and expected user information needs and promotes modular, incremental development that aligns with contemporary database design principles. This systematic approach fosters effective, cost-efficient, and scalable management information systems in diverse enterprise contexts.

Paper For Above instruction

The development of comprehensive management information systems (MIS) is pivotal for modern enterprises seeking efficient control, decision-making, and strategic planning capabilities. Central to the success of these systems is the underlying database, which must be meticulously designed to accurately reflect the organizational domain, user needs, and future growth. The methodology delineated by Chudinov et al. (2017) offers a structured framework that emphasizes the significance of the conceptual information model in establishing a robust, flexible, and scalable database system tailored for organizational management contexts.

The initial phase of this methodology involves a comprehensive semantic analysis of the domain, focusing on gathering user requirements expressed through diverse formats like documents, requests, and existing data files. This stage ensures that the model captures the essential entities and attributes relevant to organizational processes. By rigorously identifying attributes, their domains, and potential multiple values, the approach guarantees that the basic building blocks of the database are both accurate and comprehensive. Critical to this phase is assessing attribute compatibility and data integrity, which lays the groundwork for effective normalization and encoding strategies.

Normalization, as outlined in the methodology, plays a vital role in eliminating redundancy and ensuring data consistency. It involves formal rules that structure attributes into optimal schemas, often requiring attribute compression and classification. Particularly noteworthy is the criterion for encoding attributes with extensive dictionaries of possible values—where the analysis considers the size of attribute values, the number of tuples, and the potential for storage optimization. This nuanced decision-making process enhances database performance by balancing storage efficiency against query complexity.

The second stage emphasizes establishing relationships among normalized entities through algorithms designed to identify hierarchies, subordination links, and transitive associations. This relational structuring is crucial for accurately representing organizational objects and their interdependencies. The methodology advocates for a formalized approach to relationships, considering the nature of the connections—hierarchical, join, or transitive—and ensuring that the model's integrity aligns with real-world organizational logic.

In the final stage, the pooled data schemas are integrated into a unified conceptual information model, serving as a blueprint for database implementation. This model encapsulates the logical structure, relationships, and data domains, providing a clear, formal representation of organizational objects and their interactions. It facilitates subsequent physical database design and offers a robust foundation adaptable to changing user requirements and organizational growth.

Overall, this methodology offers several advantages, including incremental development, formalization of user needs, and adaptability to evolving organizational landscapes. Its systematic, step-by-step approach reduces complexity, promotes clarity, and enhances the reliability of management information systems. By emphasizing formal analysis, normalization, and relationship modeling, it aligns with best practices in database design and ensures that organizational data is stored efficiently, consistently, and accurately.

The methodology's applicability extends across different organizational sectors, accommodating various data types and operational needs. It supports the iterative refinement necessary for dynamic environments and promotes collaboration between analysts and organizational stakeholders. Such a structured yet flexible approach is indispensable for developing MIS that are not only effective in current operations but also adaptable to future challenges and technological advancements.

In conclusion, the formalized, multi-stage methodology presented by Chudinov et al. (2017) provides a comprehensive blueprint for designing management information system databases grounded in semantic clarity, normalization rigor, and relational integrity. Its emphasis on integrating user requirements with organized data schemas ensures the creation of efficient, reliable, and scalable management systems capable of supporting organizational growth and strategic initiatives for years to come.

References

• Chudinov, I. L., Osipova, V. V., & Bobrova, Y. V. (2017). The methodology of database design in organization management systems. Journal of Physics: Conference Series, 012030. https://doi.org/10.1088/
• ANSI/X3/SPARC. (1975). Study Group on Data Base Management Systems. Bulletin of ACM SIGMOD International Conference on Information Technologies in Business and Industry.
• Navathe, S. B. (1992). Evolution of Data Modeling for Databases. Communications of the ACM, 123.
• Kim, Y. G. (1995). Comparing Data Modeling Formalisms. Communications of the ACM, 115.
• System analysis methodologies in database design—C. J. Date (2003). An Introduction to Database Systems.
• Barker, R. (1990). Case*Method: Entity Relationship Modelling. Addison Wesley Professional.
• Codd, E. F. (1970). A Relation Model of Data for Large Shared Data Banks. Communications of the ACM, 387.
• Codd, E. F. (1979). Extending the Database Relational Model to Capture More Meaning. ACM Transactions on Database Systems, 4, 397–434.
• Osipova, V. V., & Seitz, J. (2011). A Formal Approach to Design a Conceptual Information Model of the Universe of Discourse. Proceedings of the Tenth Wuhan International Conference on E-Business, 567–569.
• Osipova, V. V., Chudinov, I. L., & Seidova, A. S. (2016). Formalized Approach in Relational Database Design. Key Engineering Materials – International Conference on Information Technologies in Business and Industry, 012030. https://doi.org/10.1088/