Fuzzy Counting with Probabilistic Data Structures

This Advanced Data Structures course project explores typo-tolerant fuzzy counting with compact probabilistic data structures.

Key Features

• Fuzzy matching: Implemented typo-tolerant matchers using Bloom filter segmentation and Cuckoo filters.
• Cardinality estimation: Implemented and compared HyperLogLog and HyperLogLog-LDP.
• Evaluation: Measured accuracy-space tradeoffs over synthetic typo datasets under different sketch parameters and segmentation strategies.
• Reusable tooling: Exposed the implementation as a Python library backed by the C++ experiments.

You can view the complete source code and the full academic report from the links below:

• View Code on GitHub
• Read the Full Report (PDF)
• Presentation Slide(PDF)