Event-Based Data Intake System for Unstructured Machine Data

The patent implements a universal data processing architecture that can handle both structured and unstructured data through a common pipeline. The system uses a unified event model and standardized processing stages (ingestion, parsing, enrichment, routing) that work across diverse data types including logs, metrics, traces, and unstructured text, eliminating the need for separate processing systems for different data formats.

The patent introduces intermediary components such as parsers, enrichment services, and routing mechanisms that mediate between raw unstructured data and the structured processing pipeline. These intermediaries transform diverse unstructured inputs into standardized events with uniform schemas, enabling the core processing system to handle variety without increasing its own complexity.

The patent applies preliminary filtering and enrichment actions during data ingestion before full storage occurs. The system performs initial parsing, validates data quality, enriches events with contextual information, and routes data to appropriate storage locations based on predefined criteria. This preliminary processing ensures that only relevant, high-value data is retained in full detail, reducing overall storage requirements while maintaining analytical completeness.

The patent segments data storage into multiple tiers and categories based on data type, age, and importance. Hot data (recent, frequently accessed) is stored in high-performance storage, while cold data (historical, less accessed) is archived in lower-cost storage. This segmentation allows the system to maintain complete data retention policies while optimizing storage resource utilization and reducing overall storage costs.

The patent extracts semantic meaning through preliminary actions performed during data ingestion and initial processing stages. Parsers identify key entities, events, and attributes in unstructured data, and enrichment services add contextual metadata and semantic annotations before the data enters the main analysis pipeline. This preliminary semantic extraction reduces the computational burden on downstream analysis systems and enables faster querying and interpretation.

The patent replaces complex mechanical text analysis processes with more efficient computational approaches. Instead of using resource-intensive natural language processing for all data, the system employs lightweight pattern matching, regular expressions, and predefined schemas for common data types. Machine learning models are used selectively for complex semantic tasks rather than universally, reducing overall processing time while maintaining semantic accuracy.

The patent implements a dynamic data analysis architecture that adapts to user needs in real-time. The system maintains pre-configured analysis pipelines for common use cases that provide fast, focused results, while simultaneously allowing users to dynamically create new queries, filters, and analysis parameters on-demand. The underlying event model and metadata structure enable flexible ad-hoc analysis without sacrificing the efficiency of predefined workflows.

The patent creates a universal query and analysis framework that serves both predefined and custom analysis needs through a single system. The standardized event schema and metadata structure allow the same data pipeline to support both rapid execution of pre-specified analysis templates and flexible exploration of new data aspects. Users can switch between focused predefined analyses and open-ended investigations without changing the underlying system architecture.