Big Data Approach Student's Name ✓ Solved

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6 BIG DATA APPROACH Big Data Approach Student's name

As a result of the enormous amount of data required for analysis, the 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 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.

The use of semi-custom applications will form a basis for handling big data. This technique will employ machine learning and artificial intelligence. The 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 the treatment of diabetes.

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 times. Therefore, it takes a very short time to deploy these applications. When a semi-customized application is well constructed, they offer stability by providing 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.

According to statistics by the World Health Organization, the prevalence of diabetes disease is about 9% in the United 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 with sensor-based networks (Aazam, et al, 2014).

The Internet of Things will support the collection of data through miniaturized sensors. These miniaturized sensors will 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.

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 help consolidate data from all remote locations, 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 enable data in the database to be up to date. This will enhance its reliability and therefore give 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 one is suffering from will be enhanced. This will enable easier querying and analyzing data from the database. Modern technologies have come up with formats that enable easier storage of biodata. Among the formats is Next Generation Sequencing. Health-cop company intends to use this database format due to its suitability for storing biodata (Banerjee, & Sheth, 2017).

Additionally, the database format is advantageous 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 to store and analyze the data more efficiently.

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.

Paper For Above Instructions

The rise of big data in healthcare has led to transformative changes in how medical organizations operate and improve patient outcomes. The adoption of big data approaches allows health-oriented organizations to manage, analyze, and utilize vast amounts of data efficiently. This paper explores the significance of the big data approach, its origins, the methods employed for data storage, and the implications of using semi-custom applications within the healthcare space.

Big data refers to the extremely large datasets that can be analyzed computationally to reveal patterns, trends, and associations, particularly relating to human behavior and interactions. This approach dates back to the early 2000s when advancements in computer storage and processing capabilities enabled organizations to capture and analyze large volumes of empirical data efficiently (Chen et al., 2014). The healthcare industry, often characterized by complex data from diverse sources, stood to gain immensely from the insights derived from big data analytics.

One essential aspect of the big data approach is its ability to enhance the statistical power of analyses. Through systematic data analysis using machine learning and artificial intelligence algorithms, healthcare organizations can derive actionable insights. For example, by utilizing advanced analytics, health practitioners can identify at-risk patients and tailor preventive measures accordingly (Li et al., 2019). The application of big data not only enhances organizational efficiency but can also significantly improve patient care and outcomes.

The challenges organizations face when handling big data are multifaceted. These include the analysis, storage, capture, and visualization of data, alongside concerns regarding information privacy. Addressing these challenges requires robust strategies and technological solutions, such as cloud computing, to ensure data integrity and accessibility in real-time (Jiang et al., 2014).

Cloud storage has emerged as a vital solution for data management in the healthcare sector. It provides organizations with the flexibility to store vast amounts of data securely while ensuring ease of access for authorized personnel. The one-to-many replication feature employed by cloud platforms enhances data reliability by preventing a single point of failure (Sookhak, 2015). Furthermore, with the advent of the Internet of Things (IoT), healthcare organizations are better equipped to collect data continuously from patients through devices embedded with sensors, further enriching the datasets available for analysis.

In addition to cloud storage, the choice of database formats is crucial for effective data management. Next Generation Sequencing (NGS) provides a means to store complex biodata efficiently. Its advantages include facilitating data mining techniques that harness machine learning to categorize and analyze data based on specific parameters, such as disease type (Banerjee & Sheth, 2017). By opting for advanced database formats, organizations like Health-cop can ensure that they manage their data with maximum efficiency.

The role of semi-custom applications in the big data approach cannot be overlooked. Semi-customized applications facilitate quick deployment and adaptability to the organization’s specific needs, hence enhancing analytical capabilities (Eapen & Peterson, 2015). Such applications, when well developed, provide automated insights derived from large data repositories, assisting healthcare providers in clinical decision-making.

Moreover, the implementation of machine learning models can lead to predictive analytics in healthcare, thus improving preventive care. By sifting through vast datasets, healthcare organizations can discover potential health trends and adjust their intervention strategies accordingly. For example, by analyzing dietary behaviors and their links to specific health conditions, organizations can proactively address lifestyle-related diseases such as diabetes (Larose, 2015).

Implementing a comprehensive approach to data handling entails not just effective storage solutions but also strong governance policies to uphold data security. A content security policy, tailored to the specifications of the cloud platform used, can safeguard against various cyber threats (Patil & Frederik, 2016). Regular assessments and updates of security protocols ensure that patient information remains protected while maintaining compliance with data protection standards.

The organizational structure of a healthcare analytics company like Health-cop reflects the importance of clear roles in supporting its operations. Leadership can drive innovative technology adoption, such as cloud computing for their services, while specialized departments focus on data mining, analytics, and client relations.

In conclusion, the big data approach is paramount for health-oriented organizations seeking to thrive in today's data-driven landscape. By embracing the data management strategies discussed — including semi-custom applications, cloud storage, and advanced database formats like NGS — organizations can navigate the complexities of big data while enhancing service delivery and improving patient health outcomes.

References

• Aazam, M., Khan, I., Alsaffar, A. A., & Huh, E. N. (2014). Cloud of Things: Integrating the Internet of Things and cloud computing and the issues involved. In Proceedings of the International Bhurban Conference on Applied Sciences & Technology (IBCAST) Islamabad, Pakistan.
• 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).
• Eapen, Z. J., & Peterson, E. D. (2015). Can mobile health applications facilitate meaningful behavior change?: time for answers. Jama.
• 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).
• Li, Y., Li, G., Wang, T., Zhu, Y., & Li, X. (2019). Semi-customized Design Framework of Container Accommodation for Migrant Construction Workers. Journal of Construction Engineering and Management, 145(4).
• Larose, D. T. (2015). Data mining and predictive analytics. John Wiley & Sons.
• Patil, K., & Frederik, B. (2016). A Measurement Study of the Content Security Policy on Real-World Applications. IJ Network Security, 18(2).
• Sookhak, M. (2015). Dynamic remote data auditing for securing big data storage in cloud computing (Doctoral dissertation, University of Malaya).

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