B2B catalogs, healthcare patient registries, and multi-vendor marketplaces inevitably accumulate duplicate and inconsistent records—such as "HRS" vs. "Hirose Electric Co., Ltd.", or mistyped patient names and part numbers. Resolving these variations accurately requires entity resolution, phonetic/trigram fuzzy matching, and canonical taxonomy alignment.
If you are looking to hire a freelance entity resolution, fuzzy matching, and data cleaning programmer in Python, PostgreSQL (`pg_trgm`), and Elasticsearch, I am Aldian Fazrihady.
Entity Resolution & Fuzzy Search Case Studies
- Manufacturer Name Normalization in Python: Resolving String Variations ('HRS' vs. 'Hirose') in Python
- Fuzzy Matching in Electronic Component Search: Tuning Elasticsearch for Complex Part Numbers
- Edge N-Grams & Case-Insensitive Lookups: Tuning Elasticsearch for Complex String Lookups