A Deep Analysis of Arisyn’s Product CapabilitiesAn Automatic Data Relationship Platform for Real-World Data Integration

1. Arisyn’s Mission

In one sentence:

Arisyn’s mission is to enable automatic data relationship discovery in data integration scenarios.

This definition can be further expanded as follows:

1. Application ScopeArisyn is designed for data integration use cases.

   • At the simplest level, this includes relationships between multiple tables within the same system.

   • At higher complexity, it supports relationship discovery across heterogeneous, multi-source data environments.

2. Core Requirement of Data IntegrationAny data integration task fundamentally requires the establishment of inter-table relationships.

3. PreconditionThe data to be integrated must contain implicit or explicit relational signals that can be discovered.

Under these conditions, Arisyn’s goal is straightforward:

When users specify the data they want to integrate—expressed as a set of tables and fields—Arisyn automatically generates valid and usable data relationship paths.

2. What Problem Does Arisyn Solve?

To illustrate Arisyn’s value, we use a deliberately complex but realistic example.The complexity is intentional: it is precisely in such scenarios that Arisyn’s product value becomes evident.

3. Example Scenario: A University Data Environment

3.1 Department Information (Table T_A)

Each department has a short identifier.

DEPARTMENT_ID	DEPART_NAME
GEO	School of Geology
IT	School of Information Engineering

3.2 Class Information (Table T_B)

Each department contains multiple classes.Class IDs are composed of enrollment year + sequence number.

CLASSES_ID	CLASSES_NAME	DEPARTMENT
2020_01	Geo 20 (1)	GEO
2020_02	Geo 20 (2)	GEO

(Highlighted fields represent target output fields in later steps.)

3.3 Student Information (Table T_C)

Each student has a globally unique student ID.

STUDENT_ID	STUDENT_NAME	CLASSES
202000001	Zhang San	2020_01
202000002	Li Si	2020_02

3.4 Course Information (Table T_D)

Each course has a code, full score, and credit value.

CLASS_CODE	CLASS_TITLE	FULL_SCORE	CREDIT
MATH_01	Advanced Math I	100	4
MATH_02	Advanced Math II	100	4
ENGLISH_LV1	English I	100	2
CHEMISTRY	Chemistry	10	2

3.5 Department-Specific Passing Criteria (Table T_E)

Different departments have different passing thresholds for the same course.

DEPARTMENT	CLASS	PASS_SCORE
GEO	MATH_02	60
IT	MATH_02	75

3.6 Student Exam Results (Table T_F)

Each student receives scores per course per term.

STUDENT_ID	TERM	CLASS	SCORE
202000001	2023_1	MATH_02	85
202000001	2023_1	ENGLISH_LV1	92
202000002	2023_1	MATH_01	78

4. The Business Requirement

List each student’s courses in the 2023_1 term, their achieved scores, and the corresponding passing score.

Expected output:

Class	Name	Term	Course	Passing Score	Actual Score
Geo 20 (1)	Zhang San	2023_1	Advanced Math II	60	85
...	...	...	...	...	...

5. Why This Is Harder Than It Looks

At first glance, this looks like a standard multi-table SQL join.

In reality:

• T_F contains student scores

• T_E contains department-specific passing criteria

But these two tables alone are insufficient to produce the result:

1. All identifiers are encodedCourse names, class names, and department names must be resolved.

2. Student-to-department mapping is indirectStudents belong to classes; classes belong to departments.The department is not directly stored in the exam results.

A correct SQL query therefore requires:

• Multiple intermediate joins

• Correct inference of hidden business logic

• Accurate navigation through indirect relationships

6. Where Arisyn Fits In

In this scenario:

• Blue elements represent user-defined query conditions (e.g., term = 2023_1)

• Yellow elements represent inferred tables, joins, and relationship paths

Arisyn automatically generates the yellow part.

The user only needs to specify:

• What data they want

• Under what conditions

Arisyn determines:

• Which tables must be used

• How those tables are logically connected

7. Why Manual SQL Is Not the Real Problem

Many engineers might argue:

“Writing this SQL is trivial.”

That statement is technically true — but operationally misleading.

Now place this requirement in a real enterprise environment:

• Dozens of systems (student management, research, HR, campus access, facilities, etc.)

• Hundreds or thousands of tables

• Tens of thousands of fields

• Multiple teams, vendors, and stakeholders

The requester is often:

• A business administrator, not an engineer

• Unaware of where data physically resides

The implementation team:

• May not know all systems involved

• Relies on fragmented institutional knowledge

In practice:

• SQL coding is often less than 25% of total effort

• The majority of time is spent on:

  • Requirement interpretation

  • Data source discovery

  • Data standard alignment

  • Relationship validation and trial-and-error

These efforts are:

• Manual

• Non-reusable

• Lost when people leave projects

8. The Core Problems Arisyn Solves

Arisyn fundamentally restructures the data integration workflow:

1. Developers focus on business objectives, not hidden data logic

2. Only target fields need to be specified — not intermediate tables

3. Join relationships no longer need to be manually designed

By addressing these issues, Arisyn delivers:

• Elimination of most data analysis overhead

• Dramatic reduction in trial-and-error

• Up to 90% reduction in integration workload

• 10× or greater overall efficiency improvement

9. Summary

Arisyn does not make SQL faster.

It makes data integration fundamentally scalable.

By transforming implicit, fragile, human-dependent data knowledge into an automated, reusable system capability, Arisyn enables data teams to focus on value creation instead of data archaeology.