Objectives
- Implement the Flajolet-Martin Algorithm in a scalable manner suitable for processing the extensive corpus of Swami Vivekananda’s works.
- Develop a robust data processing pipeline to handle the text data and generate the necessary input for the algorithm.
- Fine-tune the algorithm parameters and validate its accuracy against known cardinality benchmarks to ensure reliable estimates.
Technologies and Tools
- Programming Language: Python
- Data Processing: Pandas, Spark
- Flajolet-Martin Algorithm Implementation: Custom Python code
- Version Control: Git
- Documentation: Markdown
Expected Outcomes
- A scalable and efficient implementation of the Flajolet-Martin Algorithm tailored for the unique characteristics of Swami Vivekananda’s complete works.
- Accurate cardinality estimates for the distinct elements in the dataset.
- Documentation detailing the project methodology, implementation details, and findings.
Future Work
Potential future enhancements could include exploring other probabilistic algorithms for cardinality estimation, optimizing the algorithm further, or extending the analysis to specific subsets of Swami Vivekananda’s works.