Delimited Data Header Identification via Vector Embedding
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Solution Overview
Problem
Existing methods for converting delimited data into a tabular format are inefficient, often requiring significant processing resources and resulting in inaccurate header identification.
Innovation Solution
A system that processes delimited data by dividing it into subsets, generating vectors based on these subsets using an embedding space, reducing dimensionality, comparing vectors to identify headers, and generating a tabular data structure with distinguished headers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods process all delimited data to identify headers, then header identification can be performed, but processing resources are significantly consumed
Solution Approach 1:
The patent divides the delimited data into multiple subsets, where each subset contains a portion of the data. Vectors are generated for each subset separately, and these vectors are then combined through comparison to identify headers. This segmentation reduces the processing load on any single subset while maintaining overall accuracy in header identification.
2Measurement precision
If existing methods process all delimited data to identify headers, then complete data analysis is achieved, but processing time increases
Solution Approach 1:
The patent segments the delimited data into multiple subsets and processes each subset independently to generate vectors. This parallel processing approach reduces the overall processing time compared to analyzing the complete dataset sequentially, while the comparison of vectors from different subsets ensures accurate header identification.
3Productivity
If vector dimensionality is reduced, then processing efficiency improves, but information loss may occur
Solution Approach 1:
The patent applies dimensionality reduction to the vectors generated from data subsets, transforming high-dimensional vectors into lower-dimensional representations. This parameter change improves processing efficiency by reducing computational complexity while the subsequent comparison of reduced vectors across subsets helps preserve essential information for accurate header identification.
Data Source
AI summary
In some implementations, a data processing device may receive, from a data source, a set of delimiter-separated values and may divide the set of delimiter-separated values into a plurality of subsets. Each subset may correspond to at least a portion of a column or correspond to at least a portion of a row. The data processing device may generate a plurality of vectors, corresponding to the plurality of subsets, according to an embedding space. The data processing device may compare the plurality of vectors to each other and may select a row or a column of the set of delimiter-separated values as one or more headers based on comparing the plurality of vectors to each other. Accordingly, the data processing device may output a tabular data structure including the one or more headers distinguished from a remainder of the set of delimiter-separated values.