Derived Genetic Key Matching for Fast and Parallel Remote Patient Data Accessing from Multiple Data Grid Locations

Abstract

In recent years, grid computing has made a fast growth in many scientific experiments and research centers in the area of medicine, scientific computation. Many research works in data grid using replication algorithm and scheduling strategies for managing large data were introduced. However, replication and scheduling strategies pose significant threat when applied in biomedical area due to parallel access limitation in grid. In this paper, Derived Genetic Key Matching (DGKM) technique is introduced for quick parallel accessing of data (heart disease patient data) available in multiple grid locations. The storage key of patient data in multiple grid locations is synchronized to improve data integrity for effective disease diagnosis. DGKM in distributed grid services allows parallel and integrated data accessing with minimal key matching time. Further, Vantage Point (VP) tree indexed Berkeley key model is designed to optimize (patients) data storage at different grid locations. The proposed technique is implemented by GridSim and is tested using Cleveland Clinic Foundation Heart disease dataset. The results showed better performance in improving data access speed by 17.04% and accuracy of integral patient data from corresponding grid location by 15.13% compared to state-of-the-art works.