Machine Data Web Organizes Events for Dynamic Adaptation
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Solution Overview
Problem
Current methods for organizing and understanding machine data are inadequate due to the overwhelming volume, variety, and dynamic nature of the data, leading to brittleness and limited applicability, as they rely on predefined schemas and algorithms that struggle to adapt to changing data formats and relationships across multiple information systems and domains.
Innovation Solution
The creation of a Machine Data Web (MDW) by organizing machine data into events and dynamically linking them, allowing for continuous learning and adaptation, enabling the construction of complex activity paths and preserving data integrity across multiple sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If predefined data schemas and predetermined algorithms are used for organizing machine data, then data organization structure is established, but the system becomes brittle and cannot adapt to changing data formats and relationships
Solution Approach 1:
The patent implements dynamic data organization by replacing static predefined schemas with adaptive algorithms that automatically adjust to changing data formats. The system continuously learns from incoming data patterns and reorganizes the data structure dynamically, allowing it to maintain stability through adaptation rather than rigidity.
Solution Approach 2:
The system changes the parameters of data organization from fixed schema definitions to variable algorithms that can modify their behavior based on data characteristics. This allows the organization structure to transform its parameters (grouping criteria, hierarchy levels, relationship definitions) in response to changing data formats and relationships.
2Ease of operation
If manual approaches are used to comprehend machine data, then domain expertise can be applied to small amounts of data, but humans are overwhelmed as data size and variety grow
Solution Approach 1:
The system implements self-service by automatically performing data organization, pattern recognition, and insight generation without requiring manual human intervention. The algorithms autonomously analyze data, identify relationships, and present findings, freeing human experts from manual data processing while preserving their ability to interpret complex results when needed.
Solution Approach 2:
The patent replaces the mechanical human effort of manually analyzing data with automated computational algorithms. These algorithms process vast volumes of data at speeds and scales impossible for humans, while incorporating domain expertise encoded in the system to maintain analytical quality.
3Productivity
If automated approaches are used to work with large amounts of machine data, then data processing capacity increases, but specific methods are required for each data type requiring modification when formats change
Solution Approach 1:
The patent creates a universal data organization system that can handle multiple data types and formats through a single adaptive algorithmic framework. Rather than requiring separate methods for each data type, the system uses general-purpose algorithms that automatically adapt their behavior based on the characteristics of the input data, reducing method variety while maintaining high processing capacity.
4Stability of the object's composition
If predefined data schemas are used for data categorization, then data organization is achieved, but unexpected types of data are ignored and data categorization usefulness degrades quickly
Solution Approach 1:
The system replaces static categorization schemas with dynamic algorithms that continuously learn from data patterns. When unexpected data types are encountered, the system adapts its categorization criteria in real-time, maintaining stable organization principles while flexibly incorporating new data types without degradation of categorization usefulness.
Data Source
AI summary
Methods and apparatus consistent with the invention provide the ability to organize and build understandings of machine data generated by a variety of information-processing environments. Machine data is a product of information-processing systems (e.g., activity logs, configuration files, messages, database records) and represents the evidence of particular events that have taken place and been recorded in raw data format. In one embodiment, machine data is turned into a machine data web by organizing machine data into events and then linking events together.