Automated Textual Annotations for Non-Text Machine Data
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Solution Overview
Problem
Existing event-based machine-data intake and query systems are limited in effectively handling and searching non-text machine data, as they are designed primarily for text-based content, rendering non-text data unusable without modifying the system's tools and components.
Innovation Solution
Employing automated textual annotations to non-text machine data allows its integration and searching within a typical text-based event-based machine-data intake and query system, utilizing these annotations as the basis for indexing and search operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a text-based index is used to store and search machine data, then text-based content can be effectively indexed and searched, but non-text machine data cannot be effectively handled or searched
Solution Approach 1:
The patent introduces an intermediary layer (annotation system) that converts non-text machine data into text-based annotations. These annotations serve as a bridge between the non-text data and the existing text-based index, enabling the index to handle diverse data types without modification. The annotations capture essential information from non-text data in a format compatible with text-based searching.
2Productivity
If the system is designed primarily for text-based content, then text processing efficiency is high, but the system tools and components must be modified to handle non-text data
Solution Approach 1:
The patent segments the data handling process into two independent parts: (1) the existing text-based indexing and search system remains unchanged, and (2) a separate annotation layer processes non-text data and generates text-based representations. This segmentation allows the original system to maintain its efficiency while the annotation layer handles non-text data conversion.
Solution Approach 2:
The annotation system serves multiple functions: it processes various types of non-text data (binary files, images, audio, video), generates text-based annotations, and formats them for integration with the existing text-based index. This multi-functional approach enables a single system to handle both text and non-text data without requiring separate processing pipelines.
Data Source
AI summary
Described herein are technologies that facilitate effective use (e.g., indexing and searching) of non-text machine data (e.g., audio/visual data) in an event-based machine-data intake and query system.