Innovative Study On Database Management Systems In Modern Applications

Authors

  • Dr. Ahelam Tikotikar Assistant Professor, Khaja Bandanawaz University, Kalaburagi
  • Dr. Kyialbek Sakibaev Аssociate Professor, Department of Natural Sciences and Humanities
  • Dr. U. Srilakshmi Professor & HOD, Dept. of Computer Science and Engineering, Sridevi Women's Engineering College V.N. Pally, Hyderabad
  • Dr Rakshitha Kiran P Assistant professor, Department of CSE (Cyber Security), Dayananda Sagar College of Engineering, Bangalore, Karnataka

DOI:

https://doi.org/10.29284/n0s4rk20

Keywords:

Database Management Systems, Modern Applications, Cloud Databases, NoSQL, Data Architecture, Digital Infrastructure

Abstract

Database Management Systems (DBMS) form the backbone of modern digital applications, enabling efficient storage, retrieval, processing, and governance of data at unprecedented scale. With the rapid evolution of application domains such as cloud computing, artificial intelligence, Internet of Things (IoT), big data analytics, and real-time platforms, traditional database paradigms have undergone significant transformation. This study critically examines the evolution, architectural innovations, and functional roles of contemporary DBMS in modern applications. Moving beyond classical relational models, the paper explores distributed, cloud-native, NoSQL, NewSQL, and intelligent database systems, emphasizing their relevance in scalability, performance, data consistency, and security. Through conceptual analysis and system-level evaluation, the study highlights how modern DBMS architectures respond to emerging computational demands while addressing persistent challenges such as data integrity, latency, and ethical data governance. The paper contributes an integrative understanding of DBMS innovation, positioning database systems not merely as storage mechanisms but as adaptive, intelligent infrastructures central to digital ecosystems.

References

1. Abadi, D. J. (2012). Consistency tradeoffs in modern distributed database system design. IEEE Computer, 45(2), 37–42.

2. Abadi, D. J., Boncz, P. A., &Harizopoulos, S. (2009). Column-oriented database systems. Proceedings of the VLDB Endowment, 2(2), 1664–1665.

3. Cattell, R. (2011). Scalable SQL and NoSQL data stores. ACM SIGMOD Record, 39(4), 12–27.

4. Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107–113.

5. Hellerstein, J. M., Stonebraker, M., & Hamilton, J. (2007). Architecture of a database system. Foundations and Trends® in Databases, 1(2), 141–259.

6. Kleppmann, M. (2017). Designing Data-Intensive Applications. Sebastopol, CA: O’Reilly Media.

7. Pavlo, A., & Aslett, M. (2016). What’s really new with NewSQL? ACM SIGMOD Record, 45(2), 45–55.

8. Sadalage, P. J., & Fowler, M. (2013). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Boston, MA: Addison-Wesley.

9. Stonebraker, M., Abadi, D. J., DeWitt, D. J., Madden, S., Paulson, E., Pavlo, A., & Rasin, A. (2010). MapReduce and parallel DBMSs: Friends or foes? Communications of the ACM, 53(1), 64–71.

10. Zhang, C., Li, M., Zhao, Y., & Chen, J. (2020). Cloud database systems: Architecture, challenges, and opportunities. Future Generation Computer Systems, 108, 742–755.

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Published

2026-02-10

Issue

Section

Articles

How to Cite

[1]
D. A. Tikotikar, D. K. Sakibaev, D. U. Srilakshmi, and D. R. Kiran P, “Innovative Study On Database Management Systems In Modern Applications”, IJASIS, pp. 199–207, Feb. 2026, doi: 10.29284/n0s4rk20.

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