Data Labeling Rules for Faster Transaction Categorization
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Solution Overview
Problem
The challenge of analyzing vast amounts of event-driven data, such as financial transactions, is inefficient due to the need to process all transactions when only a portion is of interest, necessitating a method to label or categorize data for easy identification of relevant information.
Innovation Solution
A data labeling software program that creates rules to associate labels with data packets based on identified keywords, iteratively refining the labeling process to cover all data packets, including priority rules and additional conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all transactions are analyzed to ensure complete data coverage, then data completeness is improved, but analysis time and processing resources increase significantly
Solution Approach 1:
The system performs preliminary keyword identification and rule creation on a sample of transactions before analyzing the complete dataset. By pre-establishing labeling rules from a subset of data, the system can quickly categorize all transactions without re-analyzing every detail, thus maintaining completeness while reducing processing time.
Solution Approach 2:
The analysis process is segmented into distinct phases: (1) sampling transactions to identify keywords, (2) creating labeling rules from sampled data, and (3) applying rules to the complete dataset. This segmentation allows the system to handle large volumes of data efficiently by reusing established rules rather than analyzing each transaction from scratch.
2Measurement precision
If manual labeling of all transactions is performed to ensure accuracy, then labeling precision is improved, but labor requirements and processing time increase significantly
Solution Approach 1:
The system enables itself to perform labeling automatically by deriving rules from sampled data. Instead of requiring manual labeling of all transactions, the system uses the sampled data to create self-contained labeling rules that can be applied autonomously to the entire dataset, maintaining precision while dramatically improving throughput.
Solution Approach 2:
The system creates a simplified model (labeling rules) based on a sample of transactions, then copies this model to label the complete dataset. This copying approach allows consistent application of labeling logic across all data without requiring re-evaluation of each transaction, maintaining precision while scaling processing capacity.
3Quantity of substance
If comprehensive keyword identification is performed on all data packets, then keyword completeness is improved, but processing complexity and time increase
Solution Approach 1:
The system performs keyword identification on a partial sample of transactions rather than the complete dataset. This partial action is sufficient to extract representative keywords and create effective labeling rules, avoiding the excessive complexity of analyzing all data packets while still achieving comprehensive keyword coverage through statistical representation.
Data Source
AI summary
A method of creating a data labeling software program comprises receiving data packets; randomly selecting a first portion of the data packets; identifying a first set of keywords; creating a first set of rules for the data labeling software program, associating one of a plurality of labels with one or more of the first portion of the data packets according to keywords; executing the data labeling software program to label all of the data packets such that, for each data packet, one of the labels is associated therewith if the data packet includes keywords to which one rule of the first set of rules applies; identifying a second set of keywords; and creating a second set of rules for the data labeling software program, associating one of the labels with at least one of the data packets according to keywords of the second set.