Multidimensional Embedding Spaces for Data Type Detection
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Solution Overview
Problem
Existing technologies face challenges in efficiently classifying and correlating data objects, particularly in determining data types for structured datasets without additional metadata, leading to reduced usability and inefficiencies in machine learning applications.
Innovation Solution
The use of multidimensional embedding spaces customized for data objects, where each dimension corresponds to unique characteristics of the data, allows for the generation of object vectors that can be analyzed by machine learning algorithms to improve data type detection and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data type detection methods are used without additional metadata, then the system can process data files, but the accuracy of data type detection and classification is insufficient
Solution Approach 1:
The patent transforms data objects from their original structured format into high-dimensional embedding spaces. Each data object is represented as a vector with multiple dimensions, where each dimension captures different characteristics or relationships. This dimensional transformation enables the system to detect data types more accurately by analyzing patterns across multiple dimensions rather than relying on limited metadata fields.
Solution Approach 2:
The patent introduces an intermediary embedding space layer between the raw data objects and the classification task. This embedding space acts as a mediator that transforms and enriches the data representation, enabling machine learning algorithms to perform accurate data type detection without requiring additional metadata. The embedding space captures complex patterns and relationships that are not directly visible in the original data structure.
2Measurement precision
If multidimensional embedding spaces are created for data objects, then data type detection accuracy improves, but computational complexity and resource requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing embedding representations of data objects. Instead of computing high-dimensional embeddings in real-time during classification, the system pre-processes data into embedding spaces and stores these representations. This allows the actual classification task to operate on pre-computed embeddings, significantly reducing the computational complexity during the detection phase while maintaining high accuracy.
Solution Approach 2:
The patent creates a computational copy or representation of the data objects in the embedding space. Rather than working directly with the original complex data structures during classification, the system uses simplified vector representations that capture essential characteristics. This copying approach reduces computational overhead while preserving the information needed for accurate data type detection.
3Adaptability or versatility
If data objects are transformed into embedding spaces with multiple dimensions, then adaptability for machine learning techniques improves, but data processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-transforming data objects into embedding spaces during a separate preprocessing stage. This allows the actual machine learning classification to occur much faster, as the system only needs to process the pre-computed vector representations rather than transforming and analyzing raw data structures in real-time. The time-consuming embedding computation is performed upfront when data is available, not during the critical classification decision-making process.
Data Source
AI summary
Various embodiments are generally directed to techniques for creating and utilizing multidimensional embedding spaces for data objects, such as to condition the data for input to a neural network, for instance. Some embodiments are particularly directed to detecting data types for structured data based on learned metadata. In many embodiments, an embedding space for a set of data objects may be customized with a set of dimensions that correspond to various characteristics of the set of data objects. For example, a set of data objects may correspond to a table with each data object corresponding to a data entry in the table. In such examples, correlations between different columns and/or within a column of data may be identified and utilized as metadata to improve classifications by the neural network.