Data Record Feature Vector Conversion for Matching Efficiency
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Solution Overview
Problem
Existing data management systems face challenges in efficiently processing and matching data records, especially in complex scenarios with numerous attributes across different regions, leading to inefficiencies in data cleansing and record linkage.
Innovation Solution
A method involving a trained data representation learning model that converts data records into feature vectors, enabling efficient storage and processing by representing records in a compact vector space, allowing for accurate and fast matching through vector operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data records are stored in traditional formats with multiple attributes, then data completeness and accuracy are maintained, but storage space consumption increases and processing efficiency decreases
Solution Approach 1:
The patent transforms data records from traditional attribute-value format into feature vectors by changing the representation parameters. A trained data representation learning model converts multiple attributes into a fixed-dimensional vector space, where similar records have similar vectors. This parameter transformation enables efficient similarity computation through vector operations while maintaining data integrity and reducing storage requirements.
Solution Approach 2:
Instead of storing and processing complete data records with multiple attributes, the patent creates compact feature vector copies that capture the essential characteristics of records. These feature vectors serve as efficient representations for matching and comparison operations, significantly reducing the computational burden while preserving the ability to identify duplicate and similar records.
2Measurement precision
If traditional record matching methods are used, then comprehensive attribute comparison is performed, but matching complexity increases and processing time extends
Solution Approach 1:
The patent replaces traditional mechanical record-by-record attribute comparison with a learned vector space model. The data representation learning model automatically learns the mapping from attributes to vectors, and similarity is computed through efficient vector operations such as cosine similarity or Euclidean distance. This substitution dramatically reduces matching complexity while maintaining or improving accuracy through the learned semantic relationships in the vector space.
3Reliability
If complete data records are processed for matching, then all attributes are considered, but processing speed decreases
Solution Approach 1:
The patent performs preliminary action by pre-training a data representation learning model on the complete dataset to learn optimal feature vector representations. Once trained, the model can quickly convert new records into feature vectors and perform matching operations at high speed. The training phase captures all attribute relationships, enabling fast and reliable matching during the inference phase without processing complete records each time.
Data Source
AI summary
The present disclosure relates to a method comprising providing a set of one or more records, each record of the set of records having a set of one or more attributes. Values of the set of attributes of the set of records may be input to a trained data representation learning model for receiving, as an output of the trained data representation model, a set of feature vectors representing the set of records respectively. The set of feature vectors may be stored.