Simple Fuzzy Match Example with Approximate String Matcher

<p>This workflow demonstrates the power of <strong>exorbyte's Approximate String Matcher</strong> for handling noisy and inconsistent data.</p><p>We start with two tables:</p><ul><li><p><strong>Correct Names</strong>: a curated list of retailer names.</p></li><li><p><strong>Comparison Table</strong>: a set of misspelled or variant retailer names.</p></li></ul><p>Using our <strong>Approximate String Matcher node</strong>, we compare the inputs with three different algorithms:</p><ul><li><p><strong>Levenshtein Distance</strong>: captures character-level edits such as insertions, deletions, or substitutions.</p></li><li><p><strong>Positional Matching</strong>: accounts for character order and placement, robust against shifted or swapped characters. Best for fixed-format codes (e.g., IDs like "AB-1234")</p></li><li><p><strong>Longest Common Subsequence (LCS)</strong>: identifies shared sequences of characters, tolerant of gaps and rearrangements.</p></li></ul>