Overview: Read The Following Articles And Incorporate Them I

Overviewread The Following Articles And Incorporate Them Into Your Pap

Read the following articles and incorporate them into your paper. You are encouraged to review additional articles as well. Jeang-Kuo Chen. 2019. An Introduction of NoSQL Databases Based on Their Categories and Application Industries . Algorithms, vol. 12, no. 5, p. 106. Stefanos Georgiou. 2019. Software Development Lifecycle for Energy Efficiency: Techniques and Tools . ACM Computing Surveys, vol. 52, no. 4, pp. 1–33. L. Mokokwe. 2018. First Things First: Adopting a Holistic, Needs-Driven Approach to Improving the Quality of Routinely Collected Data. Journal of Global Oncology, p. 155. Yulia Shichkina. 2019. Approaches to Speed Up Data Processing in Relational Databases. Procedia Computer Science, vol. 150, pp. 131–139.

Paper For Above instruction

Effective management of data quality and database performance is essential in modern information systems. This paper explores strategies to improve data quality using the software development life cycle (SDLC), recommends actions for optimizing record selection and database performance based on quantitative data assessments, and discusses maintenance plans and activities to enhance data integrity. Additionally, it considers methods for planning proactive concurrency control, establishing lock granularities, and minimizing security risks within multiuser database environments.

Improving Data Quality through SDLC Phases

The SDLC provides a structured framework for systematic development and maintenance of databases, which can be leveraged to enhance data quality through specific tasks at each phase. Three targeted activities are identified: data profiling during the planning phase, validation during the implementation phase, and continuous monitoring during maintenance.

During the planning phase, data profiling helps identify inconsistencies, redundancies, and missing values in existing datasets. According to Mokokwe (2018), adopting a holistic, needs-driven approach enables organizations to prioritize critical data elements and reduce errors early in the development process. Data profiling tools analyze data distributions, detect anomalies, and facilitate cleansing strategies before database design advances.

In the implementation phase, rigorous data validation ensures that incoming data conforms to defined formats, rules, and constraints. This validation can include automated checks for data type consistency, range restrictions, and referential integrity. Chen (2019) emphasizes the significance of NoSQL databases in flexible validation approaches, especially in industries demanding scalability and diversified data models. Establishing validation routines reduces the introduction of erroneous data, thus improving overall data quality.

Continuous monitoring during the maintenance phase involves regular audits, integrity checks, and refining data validation rules based on evolving organizational needs. Shichkina (2019) highlights that speed-up approaches and optimization techniques enhance data processing efficiencies, enabling faster detection and correction of data issues. Together, these SDLC activities foster a cycle of continuous quality improvement.

Optimizing Record Selection and Database Performance

Quantitative data quality assessments guide the selection of records for processing, storage, and retrieval, directly influencing database performance. To optimize record selections, actions such as indexing important attributes, partitioning large tables, and implementing query optimization techniques are recommended. For instance, indexing reduces data retrieval times, as noted by Georgiou (2019), while partitioning divides extensive datasets into manageable segments that improve access speeds and maintain data integrity.

Performance enhancement also involves routine database maintenance, including updating statistics, reorganizing indexes, and purging obsolete data. Maintenance plans should encompass scheduled tasks to ensure optimal storage and quick data access. Proposed maintenance plans include:

• Regular index rebuilding and reorganization to sustain efficient access speeds.
• Automated data archiving to remove redundant or outdated records, thus reducing load.
• Periodic data quality audits to detect and correct inconsistencies, ensuring high data integrity.

Activities that promote data quality improvement include validating data entry processes, enforcing data standards, and implementing real-time error detection mechanisms. These activities minimize inaccuracies and duplications, further enhancing database performance and reliability.

Planning Proactive Concurrency Control and Lock Granularity

In multiuser environments, concurrency control is crucial to maintaining data consistency and security. Methods such as multilevel locking—ranging from row-level to table-level—are effective in balancing concurrency and data integrity. For instance, lock granularity planning involves choosing appropriate levels of locking based on transaction types and data access patterns. Shichkina (2019) discusses that fine-grained locking minimizes conflicts and deadlocks, but may increase overhead; thus, a hybrid approach often yields optimal performance.

To enhance security, especially in multiuser systems, methods like timestamp ordering and multiversion concurrency control (MVCC) can be employed. MVCC allows multiple transactions to access different data versions simultaneously, reducing contention and locking conflicts. This method not only improves system throughput but also minimizes security risks related to concurrent access, such as data leaks or unauthorized modifications.

Furthermore, proactive locking strategies involve predicting transaction conflicts and pre-emptively adjusting lock levels, thus preventing deadlocks and reducing record-level locking. These methods ensure a robust environment where security is maintained, and performance is optimized without sacrificing data integrity.

Minimizing Security Risks and Efficient System Planning

The selected concurrency control methods significantly contribute to reducing security risks by limiting unauthorized access and preventing lock-based vulnerabilities. Timestamp ordering enhances control over transaction sequences, preventing malicious or accidental data mishandling. When coupled with strict authentication protocols, these techniques effectively prevent breaches in shared environments.

To ensure effective system planning, guidelines include segregating sensitive data, implementing role-based access controls, and scheduling maintenance activities during off-peak hours to minimize user impact. Analyzing transaction volumes through data assessments (as suggested by Georgiou, 2019) helps in setting appropriate lock granularities that prevent record locking bottlenecks during peak operations.

This proactive planning approach ensures the system handles increasing transaction loads efficiently, maintains security, and sustains high performance. It also mitigates risks such as deadlocks, data leaks, and unauthorized access, creating a resilient architecture tailored for multiuser database environments.

Conclusion

In conclusion, integrating targeted tasks within the SDLC enhances data quality systematically. Optimization actions based on quantitative assessments improve database performance, while strategic maintenance plans support ongoing integrity. Employing advanced concurrency control methods and finely tuned lock granularities minimize security risks, ensuring reliable multiuser systems. These strategies collectively foster robust, secure, and high-performing database environments aligned with organizational objectives.

References

• Chen, J.-K. (2019). An Introduction of NoSQL Databases Based on Their Categories and Application Industries. Algorithms, 12(5), 106.
• Georgiou, S. (2019). Software Development Lifecycle for Energy Efficiency: Techniques and Tools. ACM Computing Surveys, 52(4), 1–33.
• Mokokwe, L. (2018). First Things First: Adopting a Holistic, Needs-Driven Approach to Improving the Quality of Routinely Collected Data. Journal of Global Oncology, 4, 155.
• Shichkina, Y. (2019). Approaches to Speed Up Data Processing in Relational Databases. Procedia Computer Science, 150, 131-139.
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