String Indexing and Matching in KNIME

<p>This workflow demonstrates how the Exorbyte <strong>Single-Field Indexer</strong> and <strong>Approximate Index Matcher</strong> can significantly improve data-matching performance through efficient indexing.</p><p></p><p>The workflow:</p><ol><li><p><strong>Loads </strong>customer data (names, email addresses, phone numbers etc.) and <strong>creates an index</strong> on the customer names</p></li><li><p><strong>Samples </strong>2,000 names from the same data</p></li><li><p><strong>Queries </strong>the index using the <strong>Approximate Index Matcher </strong>to find the best match for each of the 2,000 names out of the 100,000 indexed entries.</p></li><li><p><strong>Repeats </strong>the same operation <strong>without indexing</strong>, using KNIME's String Matcher node.</p></li><li><p><strong>Compares </strong>the execution times of the two matcher nodes.</p></li></ol><p></p><p><strong>🔎Insights</strong></p><ul><li><p><strong>Simplicity</strong>: Exorbyte’s Single-Field Indexer and Approximate Index Matcher enable straightforward index creation and efficient fault-tolerant querying.</p></li><li><p><strong>Speed</strong>: Index-based matching completes in fractions of a second, compared to several minutes when using non-indexed matching with the String Matcher node.</p></li></ul>