Algorithms and Data Structures Enhancement Narrative
Overview of the Artifact
The artifact used for this enhancement is the same animal_shelter.py CRUD module developed in CS-340. It supports the Jupyter Notebook dashboard that interacts with MongoDB. This enhancement focuses on improving algorithmic efficiency, data validation, and caching mechanisms to optimize performance and reliability.
The goal was to strengthen the computational logic behind CRUD operations and ensure efficient handling of large datasets within the analytics dashboard.
Enhancements Implemented
- Introduced data-cleaning algorithms (e.g.,
coerce_lat_long) to ensure valid latitude and longitude values. - Implemented in-memory caching at both module and UI levels to minimize redundant queries.
- Strengthened validation algorithms to enforce safe, type-correct queries.
- Optimized the read() method to include projections, pagination, and improved query efficiency.
- Expanded unit testing for caching, validation, and performance verification.
These improvements demonstrate practical use of algorithmic optimization and data structure management in a real-world application.
Reflection on the Enhancement Process
Enhancing this artifact taught me how algorithmic efficiency directly impacts user experience. Implementing caching reduced redundant queries, while validation safeguards improved data reliability.
Challenges included maintaining backward compatibility and ensuring cache consistency after database updates. I solved these through automatic cache invalidation and unit testing using mocked collections.
This experience strengthened my understanding of time complexity, performance trade-offs, and defensive programming.
Course Outcomes Addressed
- Algorithmic Design and Evaluation: Applied caching and query optimization techniques.
- Software Engineering: Maintained interface stability while improving efficiency.
- Security and Data Integrity: Implemented strict validation and safe data-handling practices.
This enhancement demonstrates my ability to design, implement, and evaluate optimized algorithms that improve both performance and reliability.