EVOLUTION OF DBMS IN THE PAST DECADE: A SYSTEMATIC LITERATURE REVIEW

Abstract

The field of database management systems (DBMS) has witnessed significant evolution over the past decade, driven by advancements in technology and increasing data complexities. This study provides a comprehensive review of the literature on DBMS from 2013 to 2022, highlighting key developments and emerging trends that have reshaped the landscape of data management. Early critiques by Stonebraker et al. (2013) and Gray et al. (2013) underscored the limitations of traditional relational databases in managing big data, leading to a paradigm shift towards hybrid and NoSQL systems. Subsequent research by Xu and Elmasri (2014) and Phan et al. (2014) advanced query processing techniques and examined NoSQL models, while Dean and Ghemawat (2015) reaffirmed the relevance of the MapReduce model. The introduction of self-driving databases by Pavlo et al. (2017) marked a significant move towards automation. Recent innovations, including blockchain integration (Gaetani et al., 2017) and natural language processing (Thorne et al., 2021), highlight the ongoing efforts to enhance data security and user interaction. This study synthesizes these contributions, offering insights into current DBMS technologies and future directions for research and development. The findings reveal a shift towards more advanced, flexible, and automated systems, addressing the growing complexities of modern data management.