Analysis of genetic data in for implementation of compression algorithm in Alzheimer's Disease database

A database to store genetic information of Alzheimer's Disease (AD) patients is necessary to spur research in development of drugs suitable for local patients. Works by Abdul Rahman et. al. had created this database. However, the database contains large amounts of redundant genetic data. The storage of uncompressed data is suboptimal because it would consume large storage space as well as slow down transmissions over networks. In this paper, we propose the integration of compression algorithm into the AD genetic database to optimize its storage space. We describe our implementation as well as the results. The compression algorithm had managed to reduce the storage space of the AD database to 38.89% of the original storage space.