Comparative Study of Disk Resident and Column Oriented Memory Resident Technique for Healthcare Big Data Management Using Retrieval Time

The rate at which information are being shared among people of diverse discipline, is continuously increasing the volume of data available for different forms of processing and storage. The channels for collecting data is increasing on daily basis; customers need to supply data to business owners; online social media keep on evolving; educational institutions are faced with keeping records of ever growing students’ enrollment and keeping their records after graduating is now a challenge; health institutions keep on experiencing unprecedented growth in child birth on daily basis and the need to keep and maintain adequate health records is a necessity. This resultant data flood has called for the need to explore new cost effective storage options and analysis techniques in other to benefit from the dividends of Big Data. Some of the approaches involve investing more on hardware storage devices, some involve exploring other locations’ facilities while some adopt improved software techniques.  This paper is presenting some of the results obtained using software techniques. In this research, an improved column vector memory resident (in-memory) database management was employed to manage Big Data in which a comparative study of Disk and Memory resident Big Data mining from the study was shown

A Case for the Adoption of an In-Memory Based Technique for Healthcare Big Data Management

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