Write An Essay On The Following Subjects Use Required Format

Write An Essay On The Following Subjectsuse Required Format With Corre

Write an essay on the following subjects Use required format with correct grammar Each subject should have a minimum of 250 words 1. Write statistics and epidemiology on HIV over years · Go to the Strayer Library to find at least 3 quality resources in this assignment. 24 Hour Login Access : User Name: SU Password: 24Houraccess! Case Study Case Study Read the following articles and incorporate them into your paper. You are also encouraged to review additional articles as well. · Chen, J.-K., & Lee, W.-Z. (2019). An introduction of NoSQL databases based on their categories and application industries . Algorithms , 12 (5), 106. Retrieved from · Georgiou, S., Rizou, S., & Spinellis, D. (2019). Software development lifecycle for energy efficiency: Techniques and tools . ACM Computing Surveys , 52 (4), 1–33. Retrieved from · Mokokwe, L., Maabane, G., Zambo, D., Ralefala, T., Shulman, L., Ramagola-Masire, D., Tapela, N., Grover, S, & Ho-Foster, A. (2018). First things first: Adopting a holistic, needs-driven approach to improving the quality of routinely collected data . Journal of Global Oncology, 155. Retrieved from · Shichkina, Y. (2019). Approaches to speed up data processing in relational databases . Procedia Computer Science , 150, 131. Write a 2–3 page paper in which you: · Recommend at least 3 specific tasks that could be performed to improve the quality of datasets, using the Software Development Life Cycle (SDLC) methodology. Include a thorough description of each activity per each phase. · Recommend the actions that should be performed in order to optimize record selections and to improve database performance from a quantitative data quality assessment. · Suggest 3 maintenance plans and 3 activities that could be performed in order to improve data quality. · Suggest methods that would be efficient for planning proactive concurrency control methods and lock granularities. Assess how your selected method can be used to minimize the database security risks that may occur within a multiuser environment. · Analyze how the method can be used to plan out the system effectively and ensure that the number of transactions does not produce record-level locking while the database is in operation. · Go to the Strayer Library to find at least 3 quality resources in this assignment. Must have Grammarly.com corrections made & have report. Must be unique & have SafeAssign report.

Paper For Above instruction

The evolution of HIV/AIDS epidemiology over the years provides significant insights into the effectiveness of public health strategies and ongoing challenges in controlling the virus. This essay discusses statistical trends, reports, and proposes robust data management techniques to enhance the quality of datasets, particularly in the context of HIV information, while also exploring database optimization methods suitable for a multiuser environment.

Historically, HIV/AIDS statistics reveal a complex trajectory marked by periods of rapid increase, stabilization, and decline, thanks to concerted efforts in education, prevention, and treatment. According to UNAIDS (2023), since the early 1980s, HIV has infected over 79 million people globally, with approximately 36 million currently living with the virus. The incidence peaked in the late 1990s before gradually declining in several regions due to effective intervention strategies. For example, in sub-Saharan Africa, the prevalence rate once exceeded 10% among adults but has decreased to approximately 4.7% by 2022 (UNAIDS, 2023). Advances in antiretroviral therapy (ART) have been pivotal, transforming HIV from a fatal disease into a manageable chronic condition. The epidemiology reports also highlight disparities based on socioeconomic, geographic, and demographic factors, emphasizing the need for targeted public health initiatives.

Improving data quality in the realm of HIV epidemiology is critical for accurate surveillance and effective policymaking. Employing the Software Development Life Cycle (SDLC), specific tasks can be designed to enhance dataset integrity. First, during the requirements gathering phase, stakeholders should identify critical data elements that influence epidemiological analysis, such as infection rates, treatment adherence, and demographic variables. Clear specifications ensure that subsequent data collection and storage procedures encapsulate all necessary information. Next, in the design phase, implementing validation rules and data standards—such as format consistency and mandatory fields—can substantially reduce data entry errors. For example, standardizing geographic identifiers and date formats enhances comparability across datasets. Finally, during the testing and deployment phase, conducting data validation audits and user acceptance testing helps identify inconsistencies or errors early, enabling corrections that preserve data accuracy and completeness over time.

From a quantitative perspective, optimizing record selections involves applying indexing strategies and query optimization techniques to accelerate data retrieval. A well-indexed database reduces response times and minimizes lock contention in multiuser environments, thus improving overall performance. Regular maintenance activities such as indexing, defragmentation, and updating statistics are vital for sustaining database efficiency. For example, implementing partitioning based on geographical regions allows for quicker access and management of localized data subsets. Additionally, routine integrity checks and data cleaning processes help eliminate duplicate or inconsistent records, thereby ensuring high-quality datasets for epidemiological analysis.

To further improve data quality, maintenance plans should include scheduled data audits, user training sessions, and schema reviews. Data audits facilitate the identification of inaccuracies or anomalies, which can then be corrected through targeted interventions. User training ensures that staff understand data entry protocols and validation procedures, reducing the likelihood of errors. Schema reviews help adapt data models to evolving epidemiological needs, ensuring that database structures remain relevant and efficient. Activities such as implementing automated validation scripts and periodic data reconciliation also support ongoing data integrity.

In multiuser database environments, proactive concurrency control and locking strategies are essential to maintain data security and integrity. Implementing fine-grained locking mechanisms, such as record-level or even field-level locks, can minimize contention and improve concurrency. Techniques like optimistic concurrency control facilitate transaction isolation without acquiring heavy locks, thus reducing the risk of deadlocks and race conditions. These approaches also contribute to minimizing security risks, as they allow controlled access to sensitive data, ensuring authorized modifications only. Effective lock management can be complemented by encryption and access controls to mitigate unauthorized data breaches, particularly important in health-related databases.

Lastly, these methods support system planning by reducing transaction conflicts, which in turn prevents excessive record-level locking during peak operations. Such strategies enable the system to scale efficiently handle concurrent transactions, maintaining high availability and performance. Proper planning ensures that the number of transactions remains manageable, avoiding bottlenecks or delays that could compromise data integrity or user experience. In conclusion, combining sound data quality improvement practices with advanced concurrency controls ensures a resilient, efficient, and secure database architecture suitable for managing sensitive health data like HIV statistics.

References

• UNAIDS. (2023). Global HIV & AIDS statistics — 2023 fact sheet. UNAIDS. https://www.unaids.org/en/resources/presscentre/pressreleaseandstatementarchive/2023
• Chen, J.-K., & Lee, W.-Z. (2019). An introduction of NoSQL databases based on their categories and application industries. Algorithms, 12(5), 106.
• Georgiou, S., Rizou, S., & Spinellis, D. (2019). Software development lifecycle for energy efficiency: Techniques and tools. ACM Computing Surveys, 52(4), 1–33.
• Mokokwe, L., Maabane, G., Zambo, D., Ralefala, T., Shulman, L., Ramagola-Masire, D., Tapela, N., Grover, S., & Ho-Foster, A. (2018). First things first: Adopting a holistic, needs-driven approach to improving the quality of routinely collected data. Journal of Global Oncology, 155.
• Shichkina, Y. (2019). Approaches to speed up data processing in relational databases. Procedia Computer Science, 150, 131.