Automatic System Landscape Output Processing for Migration Analysis
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Solution Overview
Problem
Database management systems face challenges in optimizing performance, particularly in handling complex queries and large volumes of data, which can lead to resource inefficiencies in memory and storage usage.
Innovation Solution
A system landscape output processor (SLOP) processes system landscape data using output templates and rule sets to generate an aggregate output for system migration analysis, including selecting and combining relevant objects to assess migration success.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If database management systems handle complex multidimensional analysis and high transaction volumes, then query response capability is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The system segments the monolithic database processing workload into multiple microservices that can be independently deployed, scaled, and optimized. Each microservice handles specific business logic, allowing resource allocation to be tailored to individual service requirements rather than provisioning for peak loads across all functions simultaneously.
Solution Approach 2:
The patent introduces a new dimensional approach by implementing multi-tenancy and hierarchical data modeling. Data is organized across multiple dimensions (tenant, business unit, department, etc.) allowing the system to serve multiple customers and workloads on shared infrastructure, thereby improving resource utilization while maintaining query performance through selective data access.
2Loss of time
If database systems optimize for fast query responses, then transaction time requirements are met, but memory and storage resource efficiency deteriorates
Solution Approach 1:
The system implements pre-computed materialized views and cached query results for frequently accessed data patterns. By performing computations in advance and storing results in optimized formats, the system reduces query response time without requiring excessive memory resources during actual transactions, as the heavy lifting has already been done.
Solution Approach 2:
The patent employs dynamic parameter adjustment for data storage and retrieval operations. Query optimization parameters, memory allocation, and storage formats are automatically adjusted based on workload characteristics, data access patterns, and available resources, allowing the system to maintain fast response times while adapting resource consumption to actual needs rather than static over-provisioning.
Data Source
AI summary
In some implementations, there is provided a method including receiving a system landscape output; processing the system landscape output using at least a rule set, wherein the processing using the rule set further comprises selecting a first output template from a plurality of output templates, selecting one or more first objects from a plurality of objects contained in the system landscape output, generating a first output using the first output template and the selected one or more first objects, selecting a second output template from the plurality of output templates, selecting one or more second objects from the plurality of objects contained in the system landscape output, and generating a second output using the first output template and the selected one or more second objects; and combining the first output and the second output to provide an aggregate output. Related systems, methods, and articles of manufacture are also disclosed.