🔐 0️⃣Get Your License

Use this node to request and register your exorbyte matchmaker license before running any toolbox nodes.

1. Choose Demo (30 days) or Production.

2. Enter your email (and Customer Token if production).

3. Execute the node — it sends a secure request to exorbyte team.

4. If offline, manually email the request file toknime-node-license@exorbyte.com.

5. When you receive the .lic file, reopen the node → Use available license fileand run the node → run License Activator.

⚠️ Each KNIME installation or Hub environment needs its own license.

👉 See full workflow guide: How to license exorbyte Extension

🧩 Fuzzy Customer Deduplication

Weighted, tolerant matching for Entity Resolution across inconsistent customer data.

🔎Multi-Field Fuzzy Search

Return the best matching customer records from a multi-field index using weighted, approximate, and detect-style matching.

🏷️Address Canonicalization with Alias Expansion

How aliases improve fuzzy matching recall in structured datasets.
Using the Alias Creator, common abbreviations like “St” → “Street” are expanded deterministically with controlled scoring.

📊 Explainable Entity Resolution

Explainable Entity Resolution shows why two records were identified as the same entity. Instead of returning only a match score, the workflow exposes field-level match qualities and scoring contributions for attributes such as name, address, or date of birth.

• Add Match Qualities of Search Columns – expands the matching quality for each individual column.

Output Settings

you can adjust the Min. Match Value to adjust how tolerant your search is.

📂 1️⃣ Load Data

Load the reference and query datasets that will be used for indexing and matching.

🧱 2️⃣ Indexing

Transform raw data into a structured Index Object optimized for fast and deterministic matching.
Define how each field should be interpreted and processed.

• Index Types

  • Generic – Identity (IDs, exact values)

  • Generic – Term (short strings)

  • Generic – Phrase (multi-word text)

  • Numeric – Integer / Double

• Alias Object (optional)

  • Synonym expansion (e.g., St → Street)

  • Controlled scoring via penalties

  • Anti-aliases to prevent false matches

• Character Mapping (optional)

  • Case normalization

  • Accent removal

  • Umlaut expansion

  • Whitespace normalization

🔍 3️⃣ Matching

Query the index using structured input data and apply deterministic matching logic across multiple fields.
Control how similarity is calculated, filtered, and ranked.

• Field Mapping

  • Input column → Indexed field

• Matching Modes

  • Exact (strict equality)

  • Approx (Levenshtein similarity)

  • Detect (substring match)

  • Complete (reverse containment)

• Weight

  • Controls importance of each field

• Minimum Quality

  • Threshold (0–100) for valid matches

• Filter Option

  • Enforce strict conditions vs scoring contribution

• Output Options

  • Match scores

  • Best match

  • Field-level metadata

Examples

• “St” → “Street”

• “Rd” → “Road”

• “Ave” → “Avenue”

• “Blvd” → “Boulevard”