Earticle

초록

영어

MongoDB, a document-based database, is suitable for distributed management environments of large-scale databases due to its high scalability, performance, and flexibility. Recently, as MongoDB has been widely used as a new database, many studies have been conducted including data modeling for MongoDB and studies on applying MongoDB to various applications. In this paper, we propose a data modeling method for implementing Seoul public transportation data with MongoDB. Seoul public transportation data is public data provided by the Korea Public Data Portal. In this study, we analyze the target data and find design patterns such as polymorphic pattern, subset pattern, computed pattern, and extended reference pattern in the data. Then, we present data modeling with these patterns. We also show examples of implementation of Seoul public transportation database in MongoDB. The proposed modeling method can improve database performance by leveraging the flexibility and scalability that are characteristics of MongoDB.

목차

Abstract
1. INTRODUCTION
2. SEOUL PUBLIC TRANSPORTATION DATA
3. MODELING WITH DESIGN PATTERNS
3.1 Polymorphic Pattern
3.2 Subset Pattern
3.3 Computed Pattern
3.4 Extended Reference Attribute
4. CONCLUSION
REFERENCES

키워드

저자

• Meekyung Min [ Professor, Dept. of Software, Seokyeong University, Seoul, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380