KÁIXAR: A Horizontal Fragmentation Method for Medical Databases Considering DICOM Data and Content-Based Queries

The accelerated growth of medical databases, particularly those storing images in DICOM (Digital Imaging and Communications in Medicine) format, has increased the need for efficient strategies to optimize the retrieval and querying of medical information. This work presents KÁIXAR, a horizontal fragmentation method for medical databases that integrates workload analysis, DICOM images, and content-based retrieval. The proposed method employs operation records (logs) to identify access patterns and, through the Pareto principle (80/20), selects the most representative queries for generating the fragmentation scheme. Subsequently, KÁIXAR proposes and allocates fragments by considering the hardware characteristics of servers located at the sites where the recorded queries originated. As a verification mechanism, the system enables the visualization of fragmented DICOM images and their associated metadata. Experimental evaluation showed scenario-dependent performance benefits. In an additional physical-site validation under a high-workload scenario, KÁIXAR reduced mean query latency from 108.735 ms to 15.062 ms relative to XAMANA, corresponding to an 86.15% reduction and a 7.22× speedup; the paired latency difference was statistically significant (p<0.001). These results support KÁIXAR as an efficient alternative for horizontal fragmentation in medical databases.