Centralized Intelligence System for Connected Information Assets

The patent introduces a centralized intelligence system as an intermediary between administrators and the complex data landscape. This system includes data ingestion components that collect data from multiple sources, data preparation components that clean and normalize data, and analytical engines that perform pattern detection and predictive analytics. The intermediary system handles the complexity of managing voluminous, high-velocity, and diverse data, while providing administrators with simplified pattern detection capabilities and comprehensive insights through unified dashboards and alerts.

The patent implements predictive analytics capabilities that perform preliminary actions by analyzing historical and real-time data to predict potential failures before they occur. The system uses machine learning models to identify patterns that precede infrastructure incidents, enabling administrators to take preventive measures. The system also performs automatic root cause analysis when incidents occur, eliminating the need for administrators to manually locate causes of failure and allowing them to focus on resolution rather than investigation.

The patent merges previously siloed data and analytics capabilities into a centralized intelligence system that consolidates information from diverse technological assets across different administrative domains. The system uses a unified data model that captures interconnections between different asset types and administrative groups. Analytics engines perform cross-domain pattern detection that identifies correlations between events in different silos, providing a holistic view that preserves administrator specialization while eliminating information loss from fragmented perspectives.