AIMLUX.ai - PowerGraph: Fusion (KGNN) Enterprise Automation Engineer leverages the KGNN’s ability to "cross-reference" legacy Oracle logic against the optimized architecture of the target systems.
This isn't just a translation; it is a structural optimization designed to take full advantage of IBM Power10/11 and the SAP HANA/DB2 columnar engines.
To navigate the 2027 SAP transition deadline, enterprise migrations require a hybrid approach: high-performance hardware (IBM Power Systems) and intelligence-driven analysis (KGNN/AI Consulting).
Enterprise Migration Lifecycle: Oracle to SAP/IBM RISE- Fusion (KGNN)
|
Stage |
Process & Objectives |
Aimlux.ai / KGNN Assistance |
IBM Power Advantage |
|
1. Discovery |
Map all Oracle schemas,
dependencies, and "dark data." |
Automated Ingestion: KGNN
builds a self-generating knowledge graph of your entire landscape. |
Massive Throughput: Power10
processors ingest TBs of metadata without latency. |
|
2. Assessment |
Identify custom PL/SQL,
compatibility gaps, and "Clean Core" opportunities. |
Semantic Analysis: AI
identifies logic patterns rather than just text, flagging 70%+ for
auto-remediation. |
Memory Density: Analyzes
entire 5TB+ datasets in-memory for instant results. |
|
3. Planning |
Design target architecture for SAP
HANA or IBM DB2 on RISE. |
Predictive Mapping: AI
recommends the optimal "Brownfield" or "Greenfield" path
based on graph insights. |
Certified Resilience: Architecture
design for 99.999% availability on IBM Power Virtual Server. |
|
4. Execution |
Data movement, schema conversion,
and custom code rewriting. |
Zero-ETL Transition: KGNN
automates complex mapping, reducing manual dev hours by up to 80%. |
Live Partition Mobility: Move
workloads with zero downtime during the cutover phase. |
|
5. Validation |
Ensure semantic parity and data
integrity post-migration. |
Autonomous Reconciliation: AI
validates that data "meaning" is preserved, not just the raw bits. |
Hardware Encryption: Transparent
Data Encryption (TDE) at the chip level for secure validation. |
|
6. Operations |
Post-migration tuning, AI-driven
insights, and hybrid cloud management. |
Continuous Optimization: AI
monitors the "Clean Core" to prevent future technical debt
accumulation. |
Scalability: Seamlessly
scale SAP HANA instances on-demand in the IBM RISE environment. |
Strategic Value of Aimlux.ai Consulting
Aimlux.ai doesn't just provide "labor"; it provides an Intelligence-Driven Platform that specifically addresses the risks of the 2025 deadline:
1. Risk Mitigation (The "Clean Core" Strategy)
By using KGNN to analyze legacy Oracle code, Aimlux.ai ensures that only necessary logic is moved. This aligns with SAP's Clean Core mandate for RISE, reducing future upgrade costs and ensuring your SAP HANA environment isn't cluttered with 20-year-old PL/SQL "ghosts."
2. Operational Efficiency on IBM Power10/11
For databases in the 2TB–5TB+ range, standard x86 cloud environments often struggle with memory bottlenecks. Aimlux.ai optimizes your migration for IBM Power Virtual Server, which offers:
2.5x more memory bandwidth than x86 alternatives.
Architecture consistency between on-premises Power systems and the IBM Cloud, reducing migration risks by up to 25%.
3. Accelerated ROI
AIMLUX.ai consulting - PowerGraph.ai can reduce manual mapping and remediation hours by 70-80%, the Aimlux.ai approach allows enterprises to achieve full ROI within the first 12 months, avoiding the "talent wars" and high consultant rates expected as the 2025 deadline nears.
__________________________________________________________________________
Custom Code Remediation:
Oracle Cloud is the TOP Tier of Database Costs and traditional migration is costly, AIMLUX.ai automates Custom Code Remediation (CCR) which is the single greatest bottleneck in database migration, often consuming 40% to 60% of the total project timeline. When moving from Oracle to SAP HANA or IBM DB2, legacy PL/SQL often contains complex logic, triggers, and proprietary extensions that don't translate 1:1.
Equitus KGNN automates this by treating code as a connected graph of intent in 3 Dimensions rather than just 2 in Property Graphs lines of text. Here is the step-by-step breakdown:
Step-by-Step Custom Code Remediation via Fusion (KGNN)
1. Semantic Parsing & Node Extraction
Instead of a simple "find and replace," the KGNN ingests the Oracle PL/SQL codebase and deconstructs it into nodes. These
Logic Nodes: Functions, procedures, and calculations.
Dependency Edges: How a specific trigger in Oracle affects a table that SAP HANA needs to access.
The Result: A visual "Code Map" that shows exactly which pieces of custom logic are critical and which are obsolete.
The Technical Process:
Deconstruction of "Code Intent": The KGNN doesn't just read the text of an Oracle stored procedure; it parses the Abstract Syntax Tree (AST). It breaks down the PL/SQL into "Intent Nodes"—identifying whether a block of code is performing a data transformation, a security check, or a calculation.
Relationship Mapping (The Edge Construction): While a standard parser sees a table, the KGNN sees the Edges—the invisible dependencies where a specific API call triggers a sequence of Oracle functions that ultimately impact an SAP business process.
Contextual Metadata Extraction: It extracts the "Tribal Knowledge" embedded in the database schema—identifying which legacy tables are actually core to the business and which are redundant "ghost" tables that should not be migrated to the IBM RISE cloud.
2. Pattern Matching against the "Target Ontology"
Enterprise Automation Engineer leverages the KGNN’s ability to "cross-reference" legacy Oracle logic against the optimized architecture of the target systems. This isn't just a translation; it is a structural optimization designed to take full advantage of IBM Power10 and the SAP HANA/DB2 columnar engines.
The KGNN compares the extracted Oracle patterns against a pre-built library of SAP HANA (SQLScript) and IBM DB2 (SQL PL) best practices.
Contextual Translation: It identifies if a proprietary Oracle hint (e.g.,
/*+ INDEX(...) */) has a semantic equivalent in the target database or if the target’s optimizer handles it natively.Optimization Identification: The AI recognizes "Row-based" logic in Oracle that should be converted to "Columnar-optimized" logic in HANA to take advantage of in-memory performance.
3. Impact Propagation Analysis
One change in a stored procedure can break five connected applications. The KGNN performs Change Impact Analysis:
It predicts the "downstream" effects of modifying a specific piece of custom code.
It flags "High-Centrality" code—logic that is touched by multiple business processes—requiring human-in-the-loop validation, while automating the "Leaf" nodes (isolated logic).
4. Automated "Clean Core" Synthesis
To align with SAP's Clean Core strategy (especially for RISE with SAP), the KGNN identifies custom code that can be replaced by Standard SAP Functionality.
It maps custom Oracle-side calculations to standard HANA Calculation Views.
This prevents "technical debt carry-over," ensuring the new environment is leaner than the legacy one.
5. Iterative Verification & Explainability
Unlike standard AI, the KGNN provides an audit trail. For every line of code converted:
Provenance: It shows the original Oracle source.
Reasoning: It explains why the specific target syntax was chosen.
Unit Test Generation: It automatically suggests test parameters based on the data relationships discovered in the analysis phase.
Efficiency Comparison: Custom Code Remediation
In a traditional migration, Custom Code Remediation is the single greatest bottleneck, often consuming 40% to 60% of the total project timeline. When moving from Oracle to SAP HANA or IBM DB2, legacy PL/SQL often contains complex logic, triggers, and proprietary extensions that don't translate 1:1.
Equitus KGNN automates this by treating code as a connected graph of intent rather than just lines of text. Here is the step-by-step breakdown:
Step-by-Step Custom Code Remediation via KGNN
1. Semantic Parsing & Node Extraction
Instead of a simple "find and replace," the KGNN ingests the Oracle PL/SQL codebase and deconstructs it into nodes.
Logic Nodes: Functions, procedures, and calculations.
Dependency Edges: How a specific trigger in Oracle affects a table that SAP HANA needs to access.
The Result: A visual "Code Map" that shows exactly which pieces of custom logic are critical and which are obsolete.
2. Pattern Matching against the "Target Ontology"
The KGNN compares the extracted Oracle patterns against a pre-built library of SAP HANA (SQLScript) and IBM DB2 (SQL PL) best practices.
Contextual Translation: It identifies if a proprietary Oracle hint (e.g.,
/*+ INDEX(...) */) has a semantic equivalent in the target database or if the target’s optimizer handles it natively.Optimization Identification: The AI recognizes "Row-based" logic in Oracle that should be converted to "Columnar-optimized" logic in HANA to take advantage of in-memory performance.
3. Impact Propagation Analysis : AIMLUX.ai has uses experience and automation engineer to oversee integration
One change in a stored procedure can break five connected applications. The KGNN performs Change Impact Analysis:
It predicts the "downstream" effects of modifying a specific piece of custom code.
It flags "High-Centrality" code—logic that is touched by multiple business processes—requiring human-in-the-loop validation, while automating the "Leaf" nodes (isolated logic).
4. Automated "Clean Core" Synthesis
To align with SAP's Clean Core strategy (especially for RISE with SAP), the KGNN identifies custom code that can be replaced by Standard SAP Functionality.
It maps custom Oracle-side calculations to standard HANA Calculation Views.
This prevents "technical debt carry-over," ensuring the new environment is leaner than the legacy one.
5. Iterative Verification & Explainability
Unlike standard AI, the KGNN provides an audit trail. For every line of code converted:
Provenance: It shows the original Oracle source.
Reasoning: It explains why the specific target syntax was chosen.
Unit Test Generation: It automatically suggests test parameters based on the data relationships discovered in the analysis phase.
Efficiency Comparison: Custom Code Remediation
|
Task |
Traditional Manual Method |
Equitus KGNN |
|
Dead Code Detection |
Manual Audit (Weeks) |
Automated (Minutes) |
|
Syntax Conversion |
Regex/Manual Rewriting |
Semantic Transformation |
|
Dependency Mapping |
Documentation/Guesswork |
Real-time Graph Visuals |
|
Logic Validation |
Trial and Error |
Predicted Impact Analysis |
