Faceted Search Visualization for Multi-Dimensional Data Analysis
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Solution Overview
Problem
Analyzing and searching massive quantities of machine-generated data from diverse sources is challenging due to the complexity and volume of data types and formats, requiring efficient data intake and query systems that can handle minimally processed data for flexible analysis.
Innovation Solution
The SPLUNK® ENTERPRISE system employs an event-based data intake and query system with a late-binding schema, allowing flexible data modeling and extraction rules applied at search time, enabling the storage and analysis of minimally processed machine data across disparate sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a rigid schema is used to structure data, then data processing efficiency is improved, but data flexibility and adaptability to diverse sources deteriorate
Solution Approach 1:
The patent implements a dynamic schema that evolves over time through machine learning. The schema automatically adapts to new data formats and structures from diverse sources without requiring manual reconfiguration. This allows the system to maintain processing efficiency while gaining flexibility to handle varying data types and sources.
Solution Approach 2:
The system changes schema parameters dynamically based on data characteristics. When new data sources are introduced or data formats change, the schema parameters are automatically adjusted through machine learning algorithms, enabling the system to adapt to new conditions while maintaining efficient processing.
2Adaptability or versatility
If data is minimally processed, then data flexibility for various analysis types is improved, but data processing time and system complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing data into a standardized internal representation while preserving original data for minimal processing needs. This allows the system to quickly access structured data for analysis without repeatedly processing raw data, reducing overall processing time while maintaining flexibility.
Solution Approach 2:
The patent introduces an intermediary layer between raw data and analysis operations. This intermediary schema layer provides a standardized interface that mediates between the need for minimal data processing and the requirement for efficient query processing, reducing system complexity while maintaining data flexibility.
3Adaptability or versatility
If a late-binding schema is used, then data modeling flexibility is improved, but query processing complexity increases
Solution Approach 1:
The system performs self-service by automatically inferring schema structures from data patterns. The machine learning algorithms autonomously determine field types, relationships, and data models without manual intervention, reducing query processing complexity while maintaining high data modeling flexibility.
Solution Approach 2:
The patent implements feedback mechanisms where query results and data patterns feed back into schema refinement. This continuous feedback loop allows the system to learn from actual usage patterns and automatically optimize schema structures, reducing processing complexity over time while maintaining flexibility.
Data Source
AI summary
Techniques and mechanisms are disclosed for generating and causing display of graphical interfaces which enable an interactive and flexible search results visualization process. Based on results data identified in response to execution of a search query, an interface element is displayed which enables users to select a field contained in the results data, also referred to herein as a “dimension” or “facet,” and for which a “faceted” visualization of the results data can be dynamically generated and displayed. As used herein, a faceted visualization refers to a graphical interface including display of at least two separate data visualizations generated based on a selected facet data dimension, where each separate data visualization corresponds to a distinct value of the selected facet dimension.