As a result of the enormous amount of data required for analysis, big data approach has become a necessity for health-oriented organizations. This approach enables the companies to perform analysis in a systematic way from data repositories. The structure of storing data in a dataset is also an important aspect. The approach offers better statistical power through the tools it provides to organizations (Chen, Mao, & Liu, 2014). The approach seeks to solve challenges about handling large amounts of data. The challenges range from data analysis, storage, capture, visualization as well as information privacy. This paper will, therefore, seek to discuss the big data approach, the origin of big data, methods of storing the data as well as the format of the database that will be used.
Use of semi-custom applications.
Use of semi-custom applications will form a basis for the handling big data. This technique will employ machine learning and artificial intelligence. Use of artificial and machine learning will significantly add value to the organization by providing platforms for handling big data in an efficient manner (Li, Li, Wang, Zhu, & Li, 2019). This technique will help in shaping the data analytics mindset at the Health-cop company. Customized applications will help the company convert model-based recommendations of treatment into actual insights that can be used in treatment of diabetes.
The rationale for using semi-customized applications
According to the prevailing circumstances at Health-cop company, a semi-customized application would suit the organization in a better way. Semi customized applications take relatively short development time. Therefore, it takes a very short time to deploy these applications. When a semi-customized application is well constructed, they offer stability by offering great reliability levels as well as more resilience (Eapen, & Peterson, 2015). Semi-custom applications are more flexible offering great service through an extended lifetime, adaptability as well as their scalability. Lastly, semi-customized applications offer better quality. Their package components have robust performance levels. Moreover, they offer high-quality standards due to their applicability in many environments.
Source of Big Data
According to statistics by the world health organization, the prevalence of diabetes disease is about 9% in the unites states of America. Considering these statistics, this number of people is large. Going further to consider the daily data required to be fetched each day in monitoring disease in each patient, the data collected each day is enormous. The cloud platform will offer daily data collection from patients through the use of artificial intelligence in collaboration of sensor-based networks (Aazam, et al, 2014). The internet of things will provide support for the collection of data through miniaturized sensors. These miniaturized sensors will then be controlled through artificial intelligence. Since the cloud platform uses the software as a service technique. Each patient in the Health-cop database will have their portals that they can access services from any environment. Machine learning techniques will help in identifying patients that require urgent help. Considering all these actions that are performed on the cloud platform, big data will be generated as a result.
Storage of Data
From the proposed architectures of data storage done before, data storage will be handled through cloud storage facilities. The company aims to implement a cloud data repository. The cloud platform will provide one to many replications. One to many replications will provide data reliability as a failure of one storage node will not affect the operations in the company. It will also help in consolidating data from all remote locations, therefore, enabling an analysis of data at a central point (Jiang, et al, 2014). Storage will depend on high-speed transmissions of data from the patient's local location to the cloud storage. This will enable continuous synchronization of data in the database and therefore enabling data in the database to be up to date. This will enhance its reliability and therefore giving a clear reflection of analytics. Storage in the database will also be supported by high-speed data acceleration. Cloud storage will enable the semi-customized data-intensive health support application to collect data from the sensor sources and pass it over to the cloud (Sookhak, 2015). Data obtained will be stored by using data segmentation methods. Several segments that will range according to the type of diabetes disease on is suffering from will be enhanced. This will enable easier querying and analyzing data from the database.
Database Formats
Modern technologies have come up with formats that enable easier storage of biodata. Among the formats, is the Next Generation Sequencing. Health cop company intends to use this database format due to its suitability to storing biodata (Banerjee, & Sheth, 2017). Additionally, the database format is of an advantage as it will help in providing useful data mining techniques as well as machine learning techniques that will help in inputting data into specific data types and formats. The main agenda towards choosing this format is to enable Health-cop company store and analyze the data more efficiently
Conclusion
Considering the factors in play at the Health-cop company, semi-custom applications will help the company achieve its objectives in handling big data. The Next-generation sequencing database format will enable the company to store biodata more efficiently.
References
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Banerjee, T., & Sheth, A. (2017). Iot quality control for data and application needs. IEEE Intelligent Systems, 32(2), 68-73.
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), 171-209.
Eapen, Z. J., & Peterson, E. D. (2015). Can mobile health applications facilitate meaningful behaviour change?: time for answers. Jama, 314(12), 1236-1237.
Jiang, L., Da Xu, L., Cai, H., Jiang, Z., Bu, F., & Xu, B. (2014). An IoT-oriented data storage framework in the cloud computing platform. IEEE Transactions on Industrial Informatics, 10(2), 1443-1451.
Li, Y., Li, G., Wang, T., Zhu, Y., & Li, X. (2019). Semicustomized Design Framework of Container Accommodation for Migrant Construction Workers. Journal of Construction Engineering and Management, 145(4), 04019014.
Sookhak, M. (2015). Dynamic remote data auditing for securing big data storage in cloud computing (Doctoral dissertation, University of Malaya).