Data Stocktaking System for Multi-System Landscape Misconfiguration Correction
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Solution Overview
Problem
In complex cloud landscapes, identifying and addressing inconsistencies in data objects across multiple systems is challenging due to diverse technologies, architectural approaches, and varying configuration possibilities, leading to inconsistencies in data processing and storage.
Innovation Solution
A data stocktaking system that collects and provides data inventory information to administrators and automated engines, enabling the identification of misconfigurations and inconsistencies in data objects across multiple systems. This system compares normalized and hashed object data from different systems, identifies differences, and applies reconfigurations to correct misconfigurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data objects are replicated across multiple systems with diverse technologies and architectures, then data availability and accessibility are improved, but inconsistencies and misconfigurations in data processing and storage occur
Solution Approach 1:
The system implements a feedback mechanism by continuously monitoring data objects across landscape systems, comparing their states, and automatically initiating reconfiguration processes when inconsistencies are detected. The data stocktaking system collects information about data objects in different systems, compares normalized representations, and triggers automated corrections based on the detected deviations from expected consistency states.
Solution Approach 2:
The patent introduces a data stocktaking system as an intermediary layer between multiple landscape systems. This intermediary collects data object information from various systems, normalizes it according to unified rules, compares states across systems, and coordinates reconfiguration actions. The intermediary abstracts the complexity of cross-system consistency management and provides a centralized control point for maintaining data integrity.
2Reliability
If manual identification and correction of data inconsistencies is performed, then data consistency can be improved, but resource consumption and time requirements increase
Solution Approach 1:
The system implements self-service by enabling automated detection and correction of data inconsistencies without requiring manual intervention. The data stocktaking system automatically collects data object states, compares them across systems, identifies misconfigurations, and triggers reconfiguration processes. The system serves itself by maintaining data consistency through automated feedback loops and self-correcting mechanisms.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Instead of human operators manually comparing data objects and making corrections, the system uses automated data collection, normalization, comparison algorithms, and programmatic reconfiguration mechanisms. This substitution of manual operations with automated computational processes significantly reduces resource consumption and time requirements.
3Measurement precision
If comprehensive data stocktaking is performed across all landscape systems, then misconfigurations can be identified more accurately, but system complexity and processing overhead increase
Solution Approach 1:
The system applies segmentation by dividing the comprehensive data stocktaking process into distinct modular components: data collection modules for each landscape system, normalization modules that apply consistent transformation rules, comparison modules that analyze differences, and reconfiguration modules that correct identified issues. This segmentation allows the complex task of cross-system data verification to be broken down into manageable, independently implementable units.
Solution Approach 2:
The patent utilizes parameter changes by transforming data objects into normalized representations with standardized parameters before comparison. The system applies normalization rules that convert diverse data formats, structures, and representations into a unified parameter set, enabling accurate comparison across different landscape systems. This parameter transformation approach maintains detection accuracy while simplifying the comparison process.
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for data privacy. One example method includes receiving normalized and hashed object data for multiple landscape systems in a multi-system landscape. The normalized and hashed object data from different landscape systems is compared to identify at least one difference between normalized and hashed object data between landscape systems for at least one object. At least one misconfiguration in the multi-system landscape is identified based on the at least one difference between normalized and hashed object data between landscape systems. A reconfiguration of the multi-system landscape is identified for correcting the misconfiguration; and the reconfiguration is applied in the multi-system landscape to correct the misconfiguration.