Most data linking systems rely on assumptions:
· Column names look similar
· Foreign keys are defined
· Metadata is reliable
In real enterprise systems, none of that is guaranteed.
Schemas drift.
Foreign keys are missing.
Naming conventions break.
Systems evolve independently.
So how do you discover real structural relationships without guessing?
That’s the problem Arisyn was built to solve.
Stop Looking at Names. Start Looking at Behavior.
Arisyn doesn’t treat columns as labels.
It treats them as statistical objects.
Every column has measurable characteristics:
· Distinct value count
· Null ratio
· Frequency distribution
· Domain boundaries
· Entropy patterns
Instead of asking:
“Do these column names look related?”
Arisyn asks:
“Do these columns behave like structurally compatible fields?”
This is what we call statistical field fingerprinting.
Distribution Modeling > Heuristics
Two columns can represent the same entity with completely different names.
Example:
· order_id
· source_ref
· txn_code
If their value distributions align structurally, naming becomes irrelevant.
Arisyn models:
· Domain overlap density
· Cardinality alignment
· Frequency skew similarity
· Containment likelihood
This moves relationship detection from string matching to statistical modeling.
Null Patterns Are Structural Signals
Null distribution is not noise — it’s signal.
For example:
· A reference field with near-zero nulls likely indicates strong dependency.
· A sparsely populated field may represent optional linkage.
· Correlated null behavior across tables can reveal conditional relationships.
Arisyn incorporates null pattern analysis directly into its structural fingerprint.
This reduces false positives and improves structural confidence.
Preventing Random Collisions
Naive value matching fails for small domains:
· Boolean fields (0/1)
· Status enums (1/2/3)
· Small categorical sets
Random overlaps happen constantly.
Arisyn avoids this by combining multiple statistical signals:
· Cardinality-normalized domain scoring
· Cross-distribution validation
· Confidence-weighted overlap modeling
A relationship must pass several structural checks — not just one.
Deterministic, Not Probabilistic
Many AI-driven systems “predict” relationships.
Arisyn does not predict.
It verifies.
Each detected link is supported by measurable statistical evidence across multiple behavioral dimensions.
The output is a deterministic relationship graph:
· Tables → nodes
· Verified column relationships → edges
· Multi-hop paths computed algorithmically
No training data.
No prompt engineering.
No probabilistic guessing.
Just structural verification.
Why This Matters
As more systems rely on AI-generated SQL, automated integration, and dynamic analytics, structural correctness becomes critical.
Probabilistic joins are acceptable in demos.
They are dangerous in production.
Arisyn treats relationship intelligence as infrastructure — grounded in statistical modeling rather than heuristics.
And in large-scale data systems, deterministic structure scales.
If you’re building data platforms, AI-powered query systems, or working on legacy modernization, this shift from probabilistic linking to deterministic verification is not just an optimization.
It’s an architectural requirement.
Learn more: https://www.arisyn.com
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