Data Query Pricing With Tuple Information Gain for Fair Transactions
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Solution Overview
Problem
Current data query transaction systems suffer from low computational efficiency and poor interpretability, leading to arbitrage issues and reduced transaction revenue due to speculative data consumers inferring high-priced queries from low-priced ones, which also undermines the fairness of data transactions.
Innovation Solution
A data query service transaction processing method and device based on tuple information gain, which constructs support sets for each database relation, executes auxiliary queries, calculates information gain for each tuple, and sets prices based on an information gain-based pricing function to ensure arbitrage-free transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple query pricing is used, then transaction processing is efficient, but arbitrage problems occur reducing transaction revenue
Solution Approach 1:
The patent changes the pricing parameter from simple query-based pricing to information gain-based pricing. By calculating the information gain of each tuple in the query results and summing them up, the system determines a more accurate query price that reflects the actual value provided, thereby preventing arbitrage while maintaining processing efficiency.
Solution Approach 2:
The patent replaces the mechanical arithmetic pricing mechanism with an information-theoretic pricing mechanism. Instead of using simple cost-based pricing that allows arbitrage, the system uses information gain calculations (based on entropy reduction) to determine prices, substituting a more sophisticated pricing model that inherently prevents arbitrage opportunities.
2Loss of energy
If complex pricing functions are used to prevent arbitrage, then transaction revenue is protected, but computational efficiency decreases
Solution Approach 1:
The patent segments the query pricing problem into tuple-level information gain calculations. Instead of computing complex pricing for the entire query at once, the system breaks it down into individual tuple contributions, calculating the information gain of each tuple separately and summing them up. This segmentation enables more efficient computation while maintaining accurate arbitrage-free pricing.
3Productivity
If traditional pricing methods are used, then processing is fast, but interpretability of pricing is poor
Solution Approach 1:
The patent introduces information gain as an intermediary concept that bridges the gap between computational efficiency and pricing interpretability. The information gain of each tuple serves as a transparent, interpretable metric that explains why a query has a certain price, while the computational framework maintains efficiency through systematic calculation and summation of these interpretable units.
Data Source
AI summary
The present disclosure provides a data query service transaction processing method based on tuple information gain. The method involves: constructing a support set for each relation in a database according to a data seller's specified information; constructing an auxiliary query according to the support set and a single table query input by a data consumer to obtain the results of both the original and auxiliary queries; calculating information gains of all tuples on a single table and obtaining a query price using an information gain-based pricing function; rewriting an original query and constructing multiple auxiliary queries; extracting and de-duplicating the multiple groups of query results, calculating information gains of all tuples on multiple tables, and obtaining the final query price for transaction according to the pricing function. A data query service transaction processing device, an electronic device, a computer-readable storage medium involving the method are also provided.