Operational Data Computation Engine Schema Inference
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Solution Overview
Problem
Current methods for processing operational data from disparate sources and formats are inefficient and labor-intensive, particularly in quantifying asset-related costs, as they require manual schema identification and handling of complex relational databases, leading to incomplete or inaccurate cost calculations.
Innovation Solution
An automated system that uses an inference engine to generate schemas from operational-related data, linking tables and generating fully linked tables and mapping tables to organize data into IT metric-oriented formats suitable for cost calculation models, reducing manual effort and improving data consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual schema identification is used through interviews and code examination, then schema accuracy can be achieved, but the process takes a long time and requires significant effort
Solution Approach 1:
The system performs self-service by automatically identifying schemas through inference algorithms that analyze operational data and generate schema definitions without requiring human interviews or manual code examination. The inference engine autonomously determines table structures, relationships, and data models from the operational data itself.
Solution Approach 2:
The manual mechanical process of interviewing staff and examining code is replaced with an automated computational system. The inference engine uses algorithmic processing and data analysis techniques to substitute human expert analysis, transforming a labor-intensive manual process into an efficient automated computational task.
2Ease of manufacture
If SQL View commands are used to manually construct linked tables, then certain columns can be included, but the effort required reduces the value and completeness of the final linked table
Solution Approach 1:
The system provides a universal solution that automatically generates comprehensive linked tables with all necessary columns and relationships. The inference engine handles multiple functions including schema identification, table relationship determination, and complete column inclusion, eliminating the need for selective manual construction and providing a complete, adaptable linked table structure.
Solution Approach 2:
The system performs self-service by automatically determining which columns and tables to include in the linked tables through inference algorithms. Rather than requiring manual selection of specific columns via SQL View commands, the system autonomously analyzes the operational data and constructs complete linked tables with all relevant data elements.
3Productivity
If manual schema identification and SQL View construction are used, then data can be processed, but the process is labor-intensive and only certain columns are included, dramatically reducing the value of the final linked table
Solution Approach 1:
The system performs self-service by automatically identifying schemas and constructing complete linked tables through inference algorithms. The system autonomously processes operational data, determines table structures and relationships, and generates comprehensive linked tables without requiring manual intervention, thereby achieving both high productivity and complete data inclusion.
Solution Approach 2:
The labor-intensive manual process of schema identification and SQL View construction is replaced with automated computational processing. The inference engine uses algorithmic analysis to substitute manual efforts, simultaneously improving productivity by automating the process and enhancing data completeness by including all relevant columns and relationships in the generated linked tables.
Data Source
AI summary
A computation system includes a receiver, a schema generator, and a table generator. The receiver receives operational related data relating to an IT environment of an organization from remote systems. The schema generator organizes the operational related data into at least one class of IT metric-oriented data based on at least one intrinsic characteristic of the operational related data to generate a schema. The operational related data includes data tables corresponding to IT assets of the organization. The table generator processes an input table based on the schema to generate an output table. The input table includes a starting table having one or more columns. The output table identifies a subset of interrelated data included in the starting table.