Operation Objects Discovery in Multi-Cloud Data
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Solution Overview
Problem
Existing technologies face challenges in efficiently monitoring and detecting segmented operations, errors, faults, trends, patterns, and warnings across multiple cloud platforms, which complicates operations management and network efficiency.
Innovation Solution
A method and system for operation objects discovery from operation data, involving pattern matching, data profiling, field classification, and de-duplication, to identify and manage operation objects across hybrid and multi-cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operation data is collected across multiple cloud platforms for comprehensive monitoring, then monitoring coverage is improved, but data complexity and processing difficulty increase
Solution Approach 1:
The patent segments operation data into distinct fields with specific patterns (e.g., timestamps, object names, operation types, status codes). Each field type is identified and processed separately through pattern matching, allowing complex multi-cloud data to be broken down into manageable, standardized components that can be monitored independently across different platforms.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes a pattern database, data profiling module, and field classifier. This intermediary layer sits between the raw operation data from multiple clouds and the final monitoring outputs, automatically standardizing and organizing data from different sources into a unified format before analysis, thereby reducing processing complexity while maintaining comprehensive coverage.
2Measurement precision
If all operation fields are processed and analyzed, then detection precision is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by first identifying and extracting only those fields that match known patterns in the database (such as standard operation fields, error codes, and status indicators). This preliminary filtering occurs before detailed analysis, so only relevant fields are subjected to comprehensive processing. This approach maintains high detection precision for critical fields while reducing overall processing time by excluding unnecessary data.
Solution Approach 2:
The patent applies different processing qualities to different fields based on their importance and characteristics. Critical fields like error codes and status indicators receive intensive analysis with multiple validation checks, while less critical fields receive lighter processing. This local differentiation of processing quality ensures high detection precision where needed while conserving computational resources and reducing processing time overall.
3Loss of information
If duplicate operation objects are retained for comprehensive analysis, then data completeness is improved, but storage efficiency deteriorates
Solution Approach 1:
The patent creates standardized copies of operation objects with unified field names and formats. Instead of storing multiple variations of the same object with different naming conventions from different cloud platforms, the system creates a single standardized representation that captures all essential information. This copying approach maintains data completeness by preserving all operational information while improving storage efficiency through deduplication and standardization.
Data Source
AI summary
A method and system for operation objects discovery from operation data includes performing pattern matching of operation data with patterns in a database. Fields in the operation data are identified as having matching patterns with the database as first potential objects. Data profiling is performed on unmatched fields of the operation data to generate data profiles. The data profiles are field classified and second potential objects are generated. The first potential objects and the second potential objects are de-duplicated, and operation objects are generated.