System for automated configuration mapping and relationship derivation to improve the accuracy of the configuration management database (CMDB)

Abstract

A system (100) for automated configuration mapping and relationship derivation to improve the accuracy of the configuration management database (CMDB), consisting of: a data acquisition module configured to continuously collect configuration data from heterogeneous IT infrastructure sources, including cloud platforms, local systems, network devices, and virtual environments; a normalization and matching module configured to standardize the collected data, eliminate duplicates, and match the data against a predefined CMDB schema; a configuration mapping engine configured to identify and classify configuration items (Cls) based on metadata, identifiers, and behavioral patterns; a module for deriving relationships that is configured to automatically detect and derive dependencies between Cls using machine learning algorithms and pattern recognition techniques; a validation and conflict resolution module configured to check the consistency of the derived relationships and correct any data inconsistencies; a visualization and reporting module configured to provide real-time graphical representations of CI relationships and infrastructure changes; and an integration and update module configured to synchronize validated configurations and relationships with the CMDB and associated IT Service Management (ITSM) tools.

Description

[0001] The present invention relates to an automated system for improving the accuracy of a configuration management database (CMDB) by dynamically mapping configurations and deriving relationships between IT assets. It utilizes real-time data acquisition, machine learning, and pattern recognition algorithms to identify, match, and validate dependencies between infrastructure components. This reduces manual errors, ensures up-to-date data records, and improves IT service management as well as the efficiency of troubleshooting.

[0002] In large IT environments, configuration management databases (CMDBs) play a critical role in tracking assets, configurations, and their interrelationships to support change management, incident resolution, and regulatory compliance. However, traditional CMDB systems rely heavily on manual data entry or static discovery tools, often resulting in incomplete, outdated, or inconsistent configuration records. This undermines trust in the CMDB and limits its usefulness in dynamic, cloud-native, or hybrid IT infrastructures.

[0003] The main problem stems from the increasing complexity and volatility of modern IT ecosystems, where virtual machines, containers, microservices, and cloud resources frequently change their state or relationships. Manual methods or rule-based discovery tools are unable to capture these changes in real time or identify non-obvious dependencies between components. As a result, IT teams face delayed root cause analysis, configuration discrepancies, and increased risks when deploying changes.

[0004] To overcome these limitations, there is an urgent need for an automated, intelligent system that not only dynamically maps configurations but also infers accurate relationships between them without requiring predefined rules. Such a system should leverage data-driven approaches like machine learning and behavioral inference models to continuously compare CMDB entries with the actual state of the infrastructure. This will ensure CMDB accuracy, reduce human intervention, and improve operational decision-making across IT service management.

[0005] One objective of the present disclosure is to provide an automated system for configuration mapping that reduces manual effort and eliminates data entry errors when maintaining CMDBs.

[0006] Another objective of this disclosure is to enable the real-time acquisition and normalization of configuration data from various IT infrastructure sources.

[0007] Another objective of the present disclosure is to use machine learning algorithms to draw intelligent inferences about relationships between configuration elements, including hidden or indirect dependencies.

[0008] Another objective of the present disclosure is to ensure the continuous accuracy and consistency of CMDB records by automatically matching changes and resolving conflicts.

[0009] Another objective of this disclosure is to improve IT service management by providing accurate and up-to-date configuration data for incident, change, and problem management.

[0010] Another objective of the present disclosure is to provide a modular and scalable architecture that supports hybrid environments including cloud-native, local and virtualized resources.

[0011] Another objective of the present disclosure is to improve root cause analysis and reduce the mean time to resolution (MTTR) by providing precise visibility of CI relationships.

[0012] Another objective of this disclosure is to provide intuitive dashboards and visualizations for IT teams to monitor infrastructure and dependencies in real time.

[0013] Another objective of the present disclosure is to enable continuous learning and continuous improvement of CI assignment and inference accuracy through adaptive feedback mechanisms.

[0014] The present invention relates generally to an intelligent system for automated configuration mapping and relationship derivation, which aims to improve the accuracy of the CMDB (Configuration Management Database). It enables the continuous detection, validation, and updating of configuration items (Cls) and their relationships in complex IT environments. This reduces manual intervention, prevents configuration deviations, and increases the reliability of IT services.

[0015] One embodiment of the present invention comprises a data acquisition module capable of acquiring configuration data in real time from various infrastructure sources such as cloud platforms, servers, containers, and networks. The module operates continuously and supports both agent-based and agentless approaches. It forms the basis for accurate, timely, and scalable CI mapping.

[0016] Another embodiment of the invention comprises a normalization and matching module that standardizes raw data, removes duplicates, and compares it with CMDB schemas. This ensures the structural consistency and integrity of the configuration data records. Furthermore, name inconsistencies are resolved and fragmented entries are merged.

[0017] Another embodiment of the invention is a configuration mapping engine that recognizes, identifies, and classifies configuration files (Cls) based on metadata, naming patterns, and system behavior. It ensures that all critical assets are recognized regardless of their deployment environment. The mapping is adaptive and continuously updated.

[0018] Another embodiment of the invention is a relationship derivation module that uses machine learning and pattern recognition to uncover both direct and hidden relationships between Cls. This eliminates the need for manually defined dependency rules and improves the transparency of dependencies between services and applications.

[0019] Another embodiment of the invention is a validation and conflict resolution engine that automatically detects discrepancies, inconsistencies, or incorrect relationships in CI data. It corrects errors or flags them for review. This module ensures data quality prior to CMDB integration.

[0020] Another embodiment of the invention is a visualization and reporting module that displays the depicted configurations and relationships in an intuitive, real-time dashboard. It supports topology views, dependency graphs, and change tracking. IT teams can use it for improved analysis and monitoring.

[0021] Another embodiment of the invention is an integration and update module that synchronizes validated data with external CMDBs and ITSM platforms. It supports bidirectional real-time updates and APL-driven communication. This ensures that the operational CMDB is continuously synchronized with the actual infrastructure.

[0022] The present invention relates to an intelligent and automated system designed to improve the accuracy and reliability of configuration management databases (CMDBs) through real-time configuration mapping and relationship derivation. In modern IT environments, maintaining an accurate CMDB is essential for effective IT service management (ITSM), yet traditional systems struggle with data inconsistencies, outdated datasets, and a lack of transparency regarding the relationships between assets. This invention addresses these challenges with a dynamic and adaptive solution that continuously collects, normalizes, and reconciles configuration data from hybrid IT infrastructures, including on-premises, cloud, and virtual environments.

[0023] The core of the invention consists of specialized modules that work together to automate the detection, mapping, and validation of configuration items (CIs) and their interdependencies. The system begins with a data ingestion module that collects CI data from various sources. This is followed by a normalization engine that standardizes formats and eliminates duplicates. The configuration mapping engine identifies and classifies CIs, while a powerful relationship inference module uses machine learning to detect dependencies, even those not explicitly defined or visible in standard detection tools. The system also includes a validation and conflict resolution module to ensure that all mappings and relationships are correct and conflict-free before being synchronized with the C.

[0024] This invention significantly reduces the need for manual data entry and static, rule-based detection, enabling near real-time updates and continuous accuracy of the CMDB. It improves operational transparency, accelerates root cause analysis, reduces change incidents, and enhances decision-making across IT operations. By ensuring the CMDB reflects the actual state of the infrastructure, the invention enables organizations to manage IT resources, services, and changes more securely, agilely, and in compliance.

[0025] The invention is explained again below with reference to the figure. This shows: Fig. : a system (100) for automated configuration mapping and relationship derivation to improve the accuracy of the configuration management database (CMDB).

[0026] Fig.This illustrates a system (100) for automated configuration mapping and relationship derivation to improve the accuracy of the configuration management database (CMDB). The operation of the system comprises several integrated modules that work together to ensure CMDB accuracy. The data ingestion module continuously collects configuration data from various sources, including network devices, cloud services, servers, containers, and virtualization platforms. The normalization and matching module standardizes the collected data, eliminates redundancies, and aligns it with existing CMDB schemas. The configuration mapping engine identifies and maps individual configuration items (Cls) across environments based on identifiers, metadata, and behavioral patterns.The relationship derivation module applies machine learning algorithms and dependency analysis techniques to identify and derive direct and indirect relationships between configuration items (CIs). The validation and conflict resolution module ensures that the mapped configurations and derived relationships are correct and resolves any inconsistencies or overlaps with existing data. The visualization and reporting module provides real-time views of the infrastructure, CI relationships, and changes over time via dashboards and audit logs. Finally, the integration and update module synchronizes the validated results back to the CMDB and integrates them with ITSM tools to support incident, problem, and change management processes.

Claims

[1] A system (100) for automated configuration mapping and relationship derivation to improve the accuracy of the configuration management database (CMDB), consisting of: a data acquisition module configured to continuously collect configuration data from heterogeneous IT infrastructure sources, including cloud platforms, local systems, network devices, and virtual environments; a normalization and matching module configured to standardize the collected data, eliminate duplicates, and match the data against a predefined CMDB schema; a configuration mapping engine configured to identify and classify configuration items (Cls) based on metadata, identifiers, and behavioral patterns; a module for deriving relationships that is configured to automatically detect and derive dependencies between Cls using machine learning algorithms and pattern recognition techniques; a validation and conflict resolution module configured to check the consistency of the derived relationships and correct any data inconsistencies; a visualization and reporting module configured to provide real-time graphical representations of CI relationships and infrastructure changes; and an integration and update module configured to synchronize validated configurations and relationships with the CMDB and associated IT Service Management (ITSM) tools. [2] System (100) according to claim 1, wherein the data acquisition module is further configured to support both agent-based and agentless data acquisition mechanisms. [3] System (100) according to claim 1, wherein the relationship inference module uses supervised and unsupervised machine learning models to improve inference accuracy over time. [4] System (100) according to claim 1, wherein the normalization and alignment module comprises rule-based logic to detect schema inconsistencies and automatically apply transformation rules. [5] System (100) according to claim 1, wherein the validation and conflict resolution module is configured to alert users to critical inconsistencies before synchronization with the CMDB. [6] System (100) according to claim 1, wherein the visualization and reporting module generates dynamic dashboards, heatmaps and topology diagrams for the interactive exploration of infrastructure components. [7] System (100) according to claim 1, wherein the integration and update module supports bidirectional synchronization with third-party CMDB platforms and ITSM tools via API connectors.

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