Dynamic Concept Registry for Visual Analytics
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Solution Overview
Problem
Existing data visualization systems are limited by the inability to dynamically create and reuse complex analytical concepts across different workflows and datasets, as they rely on pre-defined curated static concepts that do not adapt to dynamic user interactions and multiple data sources.
Innovation Solution
Implementing a method that parses natural language inputs to identify data fields and values, generates structured queries, and creates reusable named concepts, allowing users to save and reuse these concepts across various data sources and visual analytics tools, including Tableau, through a conversational interface like Slack.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-defined curated static concepts are used in natural language interfaces for data exploration, then users can craft natural language utterances with semantic support, but the system cannot adapt to dynamic analytical concepts involving multiple attributes and conditions that users develop during analysis
Solution Approach 1:
The system transitions from static pre-defined concepts to dynamic concept creation where users can define new analytical concepts during interaction. The concept registry dynamically stores and retrieves user-defined concepts that evolve during the analytical workflow, allowing the system to adapt to emerging analytical needs without requiring complex manual curation processes.
Solution Approach 2:
Users autonomously create and register their own analytical concepts during the data exploration process. The system provides self-service capabilities where users can define concepts based on their specific analytical needs, and the system automatically manages storage and retrieval of these concepts without requiring external curation or complex administrative processes.
2Productivity
If users manually create and manage complex analytical concepts for each query, then queries can be precise and tailored to specific analysis needs, but users face repetitive work when reusing concepts across different queries and datasets
Solution Approach 1:
Users define analytical concepts once during the exploration process, and the system stores these concepts in a registry for automatic reuse. Subsequent queries can reference previously defined concepts without requiring re-definition, eliminating repetitive manual work and significantly reducing the time invested in concept management across multiple queries.
Solution Approach 2:
The system enables copying of analytical concepts from the concept registry into new queries. Users can reference existing concepts by name rather than re-specifying complex attribute combinations and conditions, dramatically improving query execution efficiency and reducing the time required for concept management while maintaining precise analytical intent.
3Adaptability or versatility
If the system supports concept reuse across multiple data sources and tools, then analytical workflows become more efficient and consistent, but the system architecture becomes more complex to manage interoperability
Solution Approach 1:
The concept registry is designed as a universal storage mechanism that can be accessed by multiple data sources and analytical tools through standardized interfaces. Concepts defined in one context can be reused across different datasets and tools without requiring source-specific implementations, enabling efficient cross-platform concept reuse while managing architectural complexity through standardization.
Data Source
AI summary
A method is provided for reusing custom concepts in visual analytics workflows. The method includes displaying a data visualization for a data source. The method also includes receiving a natural language input directed to the visualization. The method also includes parsing the natural language input to data fields and/or data values. The method also includes executing queries to data sources for retrieving results, based on the data fields and/or the data values. The method also includes generating and storing a named concept from the results, including either (i) saving underlying data as the named concept or (ii) querying the results and saving resulting data as the named concept. Saving the underlying data corresponds to saving data in an attribute. Querying the results is performed when a referenced attribute is not part of the results so a new query is issued that adds data from the referenced attributes.