Introduction
The self-healing database is mainly a concept relating to the ability of an SQL server to self-correct issues and potential problems, particularly before they become known to users. Despite authentication and access control, there are different and potential risks capable of corrupting data in a database. Data corruption remains a major issue in different data applications since corrupted data contribute to wrong decisions and disastrous actions. Besides preventive control to such risks, implementing a self-healing database system plays a critical role in detecting such corruption attacks, isolating, containing, assessing, and most importantly, repairing the damage caused, thus limiting or preventing negative consequences related to the attacks. This paper will be a written proposal focusing on the implementation of a self-healing database system.
With the current risks affecting databases, effective measures have become a major focus in different areas. Therefore, self-healing databases have become an important area of concern in different including academics. As a result, understanding how to implement such systems will play an important role in mitigating possible and potential risks that affect the database system in different ways. As a result, understanding such systems and their implementation in the database will help to ensure effectiveness and positive outcomes in dealing with different issues related to the risks affecting data in the database (Dorsey et al. 2020). These problems are common and tend to have significantly negative impacts when preventive measures are not taken and considered effectively.
Research Hypothesis
With current potential risks affecting data in databases, several developed strategies have focused on mitigating and reducing the consequences of these risks, particularly in dealing with data corruption. One of the best ways is implementing a self-healing database system that will help identify and solve such issues before they become noticeable to users and before they result in specific negative outcomes.
Definitions
Self-healing database system- as database system capable of perceiving that it may not be operating correctly or that there are some potential risks, without any external help and as a result, making any necessary adjustments to restore itself to normal operation.
Data corruption- errors in a database system that may occur during different processes like writing, storage, transmission, or processing. These errors are capable of introducing unintended changes or an affected database.
Self-healing database implementation- the process of designing or using a database system capable of making necessary changes to a database by itself when an error or an issue arises, thus preventing possible negative impacts to the data in a database.
Research Plan
An interdisciplinary process is important to study the implementation of a self-healing database and its impacts, particularly in reducing possible risks related to corruption of data in a database. The interdisciplinary process is important in this case because no single disciplinary perspective can be effective in understanding such a system and its implementation process. Therefore, the research will help analyze the intent and general interpretation of the role and impact of implementing a self-healing database (Yavuz et al. 2020, December). The paper will focus on how the implementation of the system can be achieved, particularly by creating a series of SQL Server agent jobs that will be running in a periodical manner. Besides, the paper will also focus on the miscellany of functionality importantly when working together to automatically improve the general performance of an individual’s database queries.
Besides, the paper will also focus on understanding how to improve the performance of slow-running SQL queries. To achieve this, the goal will be to recompile routines that may be slow, improve retrieval of data from different indexes, and, most importantly, update index statistics effectively from which optimal query plans can be based while focusing on creating and removing indexes where needed (Lynch & Joshi, 2020, October). Besides, the paper will also examine specific reasons influencing and contributing to the decision to implement a self-healing database system. Logically, there are different types of risks that can potentially affect a database system. These risks can potentially affect the use of data in a database system, thus having negative impacts on decision-making and other processes. As a result, the research will focus on these possible risks to understand why it would be important to implement and, most importantly, use a self-healing database system.
Conclusion
Effective preventive measures to risks and issues like data corruption in a database system are important in ensuring the safety and reliability of data. Currently, there are different ways that people can use to protect their database and prevent such issues from affecting and corrupting data. However, most of these methods are focused on providing solutions after such issues occur. A self-healing database is one of the best preventive measures that people can use. Implementing a self-healing database system will help by ensuring issues like data corruption have been resolved before a user is notified of the problem and, most importantly, before the problem leads to other negative consequences. As a result, implementing such a system will ensure effectiveness and positive outcome in protecting the database system from such problems by assessing possible risks and resolving them before they cause more damage.
References
Dorsey, L. C., Wang, B., Grabowski, M., Merrick, J., & Harrald, J. R. (2020). Self healing databases for predictive risk analytics in safety-critical systems. Journal of Loss Prevention in the Process Industries, 63, 104014.
Lynch, J., & Joshi, D. A. (2020, October). Towards Practical Self-Healing Distributed Databases. In 2020 IEEE Infrastructure Conference (pp. 1-4). IEEE.
Yavuz, A., Darville, J., Celik, N., Xu, J., Chen, C. H., Langhals, B., & Engle, R. (2020, December). Advancing self-healing capabilities in interconnected microgrids via dynamic data driven applications system with relational database management. In 2020 Winter Simulation Conference (WSC) (pp. 2030-2041). IEEE.
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