Optimizing Multi-Media Database Performance: A Comprehensive Review of Fragmentation Methods and Dynamic Access Patterns

Large databases with a lot of data and many users can be difficult to manage and slow to query. Fragmentation is a technique that can be used to improve performance by dividing the database into smaller pieces that can be stored and accessed more efficiently. This research provides a systematic overview of the literature on fragmentation methods for databases. We performed a comprehensive literature review on fragmentation techniques proposed in articles published between 2012 and 2022, aiming to gather a systematic understanding of the subject. We examined the articles to determine if they took into account multimedia data or another data type, as well as other criteria such as the use of a cost model, ease of implementation, completeness, different algorithms applied to databases, and partitioning schemes. We also investigated whether the methods were dynamic, meaning that they could be adapted to changes in the database. Our investigation discovered a significant finding about the underutilization of dynamic fragmentation approaches and cost models in the context of multi-media databases. By discovering this gap, we have thrown light on an underutilized area of database management, providing opportunities for increased efficiency and cost-effectiveness. Furthermore, we stress the critical need of addressing the integration of a heritage database into existing infrastructure, which poses a substantial difficulty in the overall database management process. This discovery necessitates additional investigation and integration of dynamic fragmentation algorithms and cost models, which will result in improved performance, scalability, and resource allocation for multi-media databases.