Association Rule Accelerator Sampling Transaction Data
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Solution Overview
Problem
Conventional database systems face challenges in processing large transaction data efficiently, accurately, and cost-effectively, making it difficult for businesses to generate timely and relevant product recommendations to customers.
Innovation Solution
A system that includes an association rule accelerator to sample transactions based on item frequency, determining frequent item sets and association rules using a selected sampling rate, which reduces computational resources required for analysis while maintaining accuracy and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional database systems process large transaction data, then data storage capacity is sufficient, but computational resources required for analysis become excessive and processing time increases
Solution Approach 1:
The patent extracts only the necessary subset of transaction data through sampling, rather than processing the entire database. The association rule accelerator selects a representative sample of transactions that captures frequent item sets and association patterns, thereby reducing computational resource consumption while maintaining analytical accuracy for recommendation generation.
Solution Approach 2:
The system segments the large transaction database into manageable samples that are processed independently. By dividing the full dataset into smaller, representative subsets, the patent enables parallel processing and reduces the computational burden on individual processing units while still capturing the essential patterns needed for accurate recommendations.
2Measurement precision
If conventional database systems analyze transaction data for recommendations, then analysis accuracy can be maintained, but processing speed becomes insufficient for timely recommendations
Solution Approach 1:
The association rule accelerator performs preliminary analysis by pre-computing frequent item sets and association rules from sampled transactions before actual recommendation requests. This preliminary processing of transaction data enables faster recommendation generation during runtime, as the system only needs to retrieve pre-computed rules rather than performing full analysis requests.
Solution Approach 2:
The system creates a sampled copy of the transaction database that preserves the essential patterns and frequent item sets needed for accurate recommendations. By working with this representative copy rather than the full original database during recommendation generation, the system achieves both accuracy and speed.
3Productivity
If conventional database systems process all transactions for association rules, then recommendation completeness is high, but cost-effectiveness decreases due to resource consumption
Solution Approach 1:
The patent changes the parameter of data volume by introducing a sampling rate that balances completeness and resource consumption. By adjusting the sampling rate and selecting appropriate sample sizes, the system maintains sufficient recommendation completeness while significantly reducing computational resource consumption and associated costs.
Data Source
AI summary
An association rule accelerator may be used to access a transaction database storing a plurality of transactions, each transaction including one or more items. The association rule accelerator also may select a sampling rate based on an item frequency of frequent items within the transaction database, relative to a sampled item frequency of sampled items within a corresponding sampled transaction database. An an association rule selector may determine, using the selected sampling rate and corresponding sampled transaction database, frequent item sets within the sampled transactions, and may further determine an association rule relating at least two items of the sampled transactions, based on the frequent item sets.