Application Landscape Pattern Detection for Configuration Anomaly Analysis
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Solution Overview
Problem
Existing data processing systems struggle to efficiently analyze and manage the complex configurations and integrations between tenants and services in a solution environment, leading to inconsistencies, errors, and increased maintenance costs.
Innovation Solution
Implementing techniques and tools to analyze snapshot data from application landscapes, detect patterns in configurations, and classify them as either anomaly or standard configurations, allowing for remedial actions to be taken to eliminate anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis of configuration data is used, then system complexity is low, but productivity and measurement precision deteriorate due to inability to efficiently analyze complex configurations and integrations
Solution Approach 1:
The patent introduces an intermediary system comprising pattern detection algorithms and configuration analysis modules that automatically process configuration data. This intermediary layer between raw configuration data and human analysts enables efficient analysis of complex integrations and patterns without requiring direct manual examination, thereby improving productivity while managing system complexity through automated mediation.
Solution Approach 2:
The patent replaces manual mechanical analysis with automated computational systems. Detection algorithms automatically evaluate configuration data, identify patterns, and classify anomalies without human intervention in the analysis process. This substitution of mechanical human analysis with automated computational mechanisms dramatically improves configuration analysis efficiency while handling complex integration scenarios.
2Reliability
If comprehensive configuration monitoring is implemented, then reliability improves through anomaly detection, but device complexity increases due to additional detection and classification systems
Solution Approach 1:
The patent implements preliminary action by continuously collecting and storing configuration snapshot data over time before anomalies occur. The system maintains a historical database of configuration states and pre-processes this data through detection algorithms, so when anomalies need to be detected, the analytical work has already been partially completed. This preliminary data collection and processing improves reliability while managing monitoring complexity through advance preparation.
Solution Approach 2:
The patent creates copies of configuration data in the form of snapshot representations that can be analyzed without affecting the live system. Multiple snapshot copies are stored historically and can be evaluated by detection algorithms independently. This copying approach enables comprehensive monitoring and anomaly detection while isolating the analysis complexity from the production system, thereby improving reliability without proportionally increasing operational complexity.
3Measurement precision
If historical data tracking is implemented, then measurement precision improves for pattern classification, but loss of time increases due to processing accumulated data
Solution Approach 1:
The patent implements periodic action by evaluating configuration snapshot data at scheduled intervals rather than continuously processing all historical data. The detection algorithms periodically analyze stored snapshots and compare them against known patterns, maintaining measurement precision for anomaly detection while avoiding the time loss of continuous real-time processing of every data point. This periodic evaluation approach balances classification accuracy with processing time efficiency.
Solution Approach 2:
The patent applies partial action by focusing detection algorithms on specific configuration elements and patterns that are most relevant to anomaly detection, rather than processing every detail of historical data equally. The system identifies and analyzes only the critical configuration parameters and integration patterns that contribute to anomaly classification, achieving sufficient measurement precision without the time cost of exhaustive complete data processing.
Data Source
AI summary
Methods, systems, and computer-readable storage media for are directed to techniques and tools for analyzing data collected at application landscapes to detect patterns in configurations of tenants, services, and integrations between tenants and services. Snapshot data representing configurations defined for customers of a solution environment can be ready. The solution environment comprises a set of landscapes. The snapshot data includes data for integrations between tenants and/or services of a set of customers. The snapshot data can be evaluated according to detection algorithms to identify patterns in configurations in the solution environment based on historized snapshot data stored over time. A first identified pattern of the patterns associated with a first configuration of the configurations in the solution environment is classified as one of an anomaly configuration type or a standard configuration type. The classifying is based on using tracked data for historically classified patterns associated with the solution environment.