Context-Adaptive Visualization Selection in Modular Frameworks
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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 variety of data types, formats, and the need for flexible data processing and visualization to extract insights effectively.
Innovation Solution
A data intake and query system, such as the SPLUNK® ENTERPRISE system, employs a late-binding schema and modular visualizations framework to process and visualize machine-generated data, allowing for flexible data ingestion, indexing, and real-time search and analysis, along with context-adapted visualization selection based on user queries and data characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a modular visualization framework with multiple visualization types is provided, then user flexibility and adaptability in data presentation are improved, but the complexity of selecting and managing appropriate visualization types increases
Solution Approach 1:
The system automatically analyzes data characteristics and user context to generate recommended visualization types, eliminating the need for users to manually evaluate multiple visualization options. The framework self-services by providing context-adaptive recommendations based on data type, query context, and user preferences.
Solution Approach 2:
The system incorporates user feedback mechanisms where user selections and interactions with visualizations are tracked and used to refine future recommendations. This feedback loop enables the system to learn from user behavior and improve visualization type suggestions over time.
2Loss of time
If context-adaptive visualization recommendations are generated automatically, then user time for visualization selection is reduced, but the computational resources required for analysis increase
Solution Approach 1:
The system performs preliminary analysis of data characteristics and user context when data is ingested or queried, preparing visualization recommendations in advance before the user needs to select them. This preliminary action reduces the computational burden at the moment of user interaction.
Solution Approach 2:
The system applies different levels of analysis intensity based on local context - simple data types receive basic visualization recommendations while complex data types require more sophisticated analysis. This localized approach optimizes computational resource allocation.
3Measurement precision
If comprehensive data analysis is performed to generate accurate visualization recommendations, then recommendation precision is improved, but processing speed decreases
Solution Approach 1:
The system performs partial analysis by focusing on the most relevant data characteristics for visualization selection rather than analyzing all possible attributes. This selective approach maintains recommendation accuracy while reducing processing time.
Solution Approach 2:
The system dynamically adjusts the depth and scope of data analysis based on the complexity of the data and the user's interaction patterns. For simple queries, minimal analysis is performed; for complex queries requiring precise recommendations, more comprehensive analysis is applied.
Data Source
AI summary
A modular visualization framework registers definitions for a variety of visualization types. The definitions are tagged with visualization characteristics. During a working session, likely interactive, a user identifies a search query used to produce data to be visualized. The working context, including the search query and data produced by its execution, is tagged for its visualization characteristics. Information about the working context, including its visualization characteristics, is then used to produce a customized list of candidates suited for the working context from which the user may select a visualization type.