OPTIMIZING VERTICAL DISTRIBUTION TECHNIQUES FOR HERITAGE MULTIMEDIA DATABASES: CHALLENGES AND SOLUTIONS

Distributed Multimedia Database Systems (DMDBSs) face critical challenges of sustaining robust performance, influenced by factors such as data fragmentation, allocation, replication, and site clustering. This research introduces an approach for vertical fragmentation of heritage multimedia databases by leveraging the silhouette analysis method alongside the K-means++ clustering algorithm. The proposed approach offers superior centroid initialization and objective cluster validation, resulting in higher cluster quality and improved data compactness. To evaluate the proposed method, experiments were conducted on a real-world dataset from the Antiquities Museum at the Bibliotheca Alexandrina. Previous approaches using K-means clustering with refinement aim to improve data localization by adjusting attribute clusters. However, such methods are limited by random centroid initialization and lack objective validation of cluster quality. Results show that our approach outperforms both the K-means with refinement method and the Differential Bond Energy Algorithm (DBEA), achieving higher fragmentation quality, better data locality, and lower execution time. These findings highlight the practical relevance of the proposed approach for enhancing the efficiency of multimedia data distribution in DMDBSs.