Phrase Recommendations for Valid Data Visualizations
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Solution Overview
Problem
Users face difficulty in identifying appropriate fields, filters, and aggregations to construct meaningful data visualizations, especially with large or complex datasets, leading to inefficiency and trial-and-error in conventional systems.
Innovation Solution
A phrase construction system that utilizes historical usage data and a probability-based machine learning model to recommend semantically consistent and syntactically correct phrases for data visualizations, providing a menu-based approach to assist users in constructing valid visualizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually search for fields, filters, and aggregations in conventional systems, then they can construct visualizations, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary action by pre-calculating and storing phrase recommendations based on historical usage data and machine learning models before the user needs to construct a visualization. The system proactively generates and displays recommended phrases, fields, filters, and aggregations in a menu-based interface, eliminating the need for users to manually search through large datasets. This preliminary preparation significantly reduces the time and effort required for visualization construction.
2Ease of operation
If users type queries to generate visualizations, then they can specify their requirements, but the system cannot provide guided assistance or prevent invalid constructions
Solution Approach 1:
The system introduces an intermediary layer in the form of a menu-based phrase construction interface. Instead of directly accepting raw user queries or allowing free-form input, the system provides a structured menu that presents pre-validated phrases, fields, filters, and aggregations. This intermediary menu guides users through the construction process, ensuring that only valid and meaningful phrases are combined to create visualizations, thereby improving both ease of operation and reliability.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user selections and updating the menu-based interface in real-time. When users select phrases or fields, the system provides immediate feedback by displaying relevant recommendations and validating the construction. This feedback loop ensures that users are guided through the process and prevents invalid constructions from being generated, enhancing both usability and reliability.
3Adaptability or versatility
If the system provides comprehensive phrase options, then users have more choices, but the complexity of the interface increases
Solution Approach 1:
The system applies segmentation by dividing the comprehensive set of phrase options into organized categories and hierarchical levels. The menu-based interface segments phrases into distinct sections such as fields, filters, aggregations, and visualizations, with each section further divided into sub-categories. This segmentation makes the interface more manageable and less complex, while still providing comprehensive and flexible phrase selection options. Users can navigate through structured groups rather than facing a flat, overwhelming list.
Data Source
AI summary
The various implementations described herein include methods and devices for recommending phrases for data visualizations. In one aspect, a method includes presenting a data visualization page to a user, the page including a first region for displaying a data visualization and a second region for phrase recommendations. The method further includes obtaining a dataset selected by the user, the dataset including a plurality of fields; and generating a first set of phrase recommendations based on the dataset, each phrase recommendation corresponding to a respective field. The method also includes displaying the first set of phrase recommendations in the second region; and receiving a user selection of a first phrase. The method further includes, in response to the user selection: presenting a data visualization in the first region using the first phrase; and displaying a second set of phrase recommendations generated based on the first phrase.