A Case for the Adoption of an In-Memory Based Technique for Healthcare Big Data Management
Keywords:Big Data, in-memory, in-disk, columnar-oriented, caching, disk-I/O, OLAP.
In healthcare organizations, the amount of data that are generated daily are on the increase with every visit by patient. The generated data through vital signs’ readings such as body temperature, pulse rate, respiratory rate, blood pressure, body weight among others are now accumulated into big data. Recently, the growth of data is averaged at about 35 percent annually. The implication is that the amount of storage needed to hold the data doubles within a period of three years. No doubt, if these data are processed and analyzed properly, it holds immense value in diagnosis and predictive medical conditions. However, the ever increasing volume of data has brought with it some big challenges. One of such is how healthcare organizations are going to store and access the vast amount of inherent information. In this paper, we discussed the need for storing medical Big Data in the main memory (In-Memory) as a way of addressing storage and access to information challenges of big data in health care delivery system. With current trends in technology advancement, there is an availability of storage systems with increased memory capacities. The storage of data in main memory can achieve a performance improvement of up to a factor of 100,000 or more. With this achievable performance, In-Memory Data Management proves to be a viable option.
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