Tree-Based Data Filtering for High Cardinality Datasets
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Solution Overview
Problem
Managing large, multivariate data sets is challenging due to high cardinality, making it difficult to explore and understand, and existing filtering methods often require expertise in creating and applying complex query statements.
Innovation Solution
The system employs multiple trees to filter data sets by selecting nodes to generate a composite filter, allowing for effective reduction of data sets into manageable portions, using either independent or cascading approaches to guide users in filtering processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional filtering methods are used, then filtering capability is achieved, but complexity of query statements increases and requires expertise
Solution Approach 1:
The patent introduces multiple trees as intermediary structures between the user and the data set. Each tree represents a dimension or attribute, and users interact with these trees through simple node selections rather than constructing complex query statements. The trees act as mediators that translate user selections into filter criteria automatically.
Solution Approach 2:
The patent segments the filtering process into multiple independent trees, each handling a specific dimension or attribute of the data. This segmentation allows users to navigate and filter data along different dimensions separately through intuitive tree traversals, avoiding the need to construct complex combined queries.
2Ease of operation
If multiple trees are used for filtering, then ease of operation improves, but device complexity increases
Solution Approach 1:
The patent divides the data set into multiple trees, where each tree represents a distinct dimension or attribute. This segmentation organizes complex multi-dimensional data into manageable, intuitive structures that users can navigate independently along each dimension.
Solution Approach 2:
The patent implements nested tree structures where trees can contain sub-trees or organized hierarchies of nodes. This nesting allows complex filtering dimensions to be organized in nested groups, making the overall structure more manageable and intuitive for users while maintaining computational organization.
3Measurement precision
If deep traversals are required, then filtering precision is achieved, but time consumption increases
Solution Approach 1:
The patent performs preliminary organization of data into multiple trees with structured node hierarchies before filtering occurs. This pre-structuring allows users to achieve precise filtering results through shorter, more direct traversals by navigating pre-organized categories rather than searching through unstructured data.
Solution Approach 2:
The patent transforms the filtering problem from a single deep traversal dimension to multiple shallower tree dimensions. By distributing filtering criteria across multiple trees representing different dimensions, users can achieve comprehensive filtering through parallel, shorter traversals rather than one long deep traversal.
Data Source
AI summary
A computing device configured for filtering a data set is described. The computing device includes a processor and executable instructions stored in memory that is in electronic communication with the processor. The computing device obtains a data set. The computing device also selects nodes from multiple trees to produce a node selection. The computing device further generates a composite filter based on the node selection. The computing device additionally filters the data set based on the composite filter to produce a working data set.