Frontend Data Visualization Grouping for Large Dataset Processing
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Solution Overview
Problem
Processing and visualizing large datasets pose challenges due to computational expense and resource overload, making conventional methods inefficient for handling millions of data objects, which can result in visually incomprehensible and analytically useless visualizations.
Innovation Solution
A system employing lightweight data object representations that group data objects based on properties, using group identifiers to reduce memory footprint and processing usage, allowing for efficient analysis and visualization of large datasets by communicating between frontend and backend components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional communication schemes are used between frontend and backend components, then complete data objects can be processed, but the communication channel becomes overloaded and the frontend component is overwhelmed with processing
Solution Approach 1:
The patent extracts only the essential identifying features of data objects (group identifiers, key properties) rather than transmitting complete data objects. The frontend sends minimal queries and receives condensed results, leaving detailed processing to the backend where resources are abundant. This extraction principle resolves the contradiction by maintaining data processing reliability while dramatically reducing communication channel load.
Solution Approach 2:
The patent introduces an intermediary layer (the backend server) that handles heavy processing responsibilities. Instead of the frontend directly processing large datasets, the backend acts as a mediator that performs computations, aggregates data, and returns simplified results. This intermediary approach maintains complete data processing capability while protecting the frontend from being overwhelmed.
2Quantity of substance
If visualizations include millions of graphical elements, then all data objects are represented, but the visualization becomes visually incomprehensible and analytically useless
Solution Approach 1:
The patent merges multiple individual data objects into aggregated groups represented by single graphical elements. Instead of displaying millions of separate points, the system clusters related data objects and represents them as consolidated visual entities with aggregated properties. This merging maintains representation of all data objects while making the visualization comprehensible by reducing visual clutter.
Solution Approach 2:
The patent applies partial action by selectively visualizing only the most relevant aggregated data groups rather than attempting to display every individual data object. The system performs sufficient aggregation to achieve comprehensibility while retaining the ability to drill down into specific groups for detailed analysis. This partial visualization approach balances complete data representation with user comprehension.
3Productivity
If processing and rendering millions of data objects is performed, then comprehensive analysis is achieved, but computational expense becomes prohibitively high
Solution Approach 1:
The patent segments the processing workload between frontend and backend components, with the backend handling computationally intensive operations. The frontend performs only lightweight tasks such as displaying aggregated results and receiving user interactions. This segmentation enables comprehensive analysis through backend processing while keeping frontend computational expense minimal.
Solution Approach 2:
The patent performs preliminary aggregation and processing of data objects on the backend before transmitting results to the frontend. By pre-computing aggregated statistics and groupings server-side, the system avoids redundant processing on the frontend. This preliminary action maintains analysis comprehensiveness while significantly reducing the computational resources required at the frontend level.
Data Source
AI summary
Systems and methods for simplified frontend processing and visualization of large datasets are provided. In example embodiments, a plurality of data objects are accessed. One or more of data objects from among the plurality of data objects are grouped into a first group. A group identifier that identifies data objects included in the first group is created. A visualization comprising a first node that represents the first group is generated. The first node is linked to the group identifier. The visualization is presented on a user interface of the user device. User input indication a user-specified action and the group identifier is received. The user-specified action is performed with the data objects identified by the group identifier.