Single-field Indexing and Matching Overview Examples

<p><strong>Fuzzy Matching &amp; Index Management with Exorbyte MatchMaker (Labs)</strong></p><p>This workflow demonstrates how to perform <strong>approximate index matching, normalization, and index management</strong> using the Exorbyte MatchMaker (Labs) nodes in KNIME.<br></p><p>It showcases key steps for enabling <strong>fault-tolerant name and text matching</strong> through configurable indexing, normalization, and license activation.</p><p>🧩 Included Examples</p><ul><li><p><strong>License Setup:</strong> How to request, activate, and manage MatchMaker licenses.</p></li><li><p><strong>Quickstart:</strong> Detecting exact and single-edit typos using Levenshtein similarity.</p></li><li><p><strong>Leading/Trailing Ignored Segments:</strong> Focusing matching on meaningful code or name parts.</p></li><li><p><strong>Character Normalization:</strong> Improving accuracy with accent and diacritic mapping.</p></li><li><p><strong>Index Writing &amp; Reading:</strong> Persisting and reusing index data across workflows.</p></li><li><p><strong>Name Variant Matching:</strong> Linking variant spellings (e.g., <em>Mathias ↔ Matthias</em>) through indexed fuzzy matching.</p></li></ul><p>🎯 Purpose</p><p>To provide a <strong>complete reference workflow</strong> for building, saving, and reusing Exorbyte indexes, and for applying <strong>approximate matching logic</strong> in realistic data-cleaning and entity-resolution tasks.</p>