Multi-Dimensional Data Unit Matching in E-Commerce Systems
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Solution Overview
Problem
Existing e-commerce systems face inefficiencies and delays in matching multi-dimensional coupon data units to complex user scenarios due to their inability to handle multi-dimensional information effectively in real-time.
Innovation Solution
A method and system that involve arranging an interface module and cache modules on a front-end processing device, processing services in multiple dimensions on a remote device, and writing processed multi-dimensional data units into cache modules, allowing for quick querying and retrieval of matched data units based on user requests, with the ability to dynamically add or reduce matching modules based on user request thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing automatic matching approaches are used for coupon data units, then the system can process user requests, but the matching efficiency is low and serious delay occurs when matching multi-dimensional coupon data units to multi-dimensional information scenarios
Solution Approach 1:
The patent segments the complex multi-dimensional matching problem into multiple independent dimensions (user dimension, merchandise dimension, channel dimension, region dimension, time dimension). Each dimension is processed separately through dedicated processing modules, allowing parallel execution and reducing overall matching time. The segmentation enables the system to handle each dimensional filter independently rather than processing the entire multi-dimensional space as a single complex operation.
Solution Approach 2:
The system performs preliminary actions by pre-processing and caching dimensional data before actual user requests arrive. Dimensional filters are pre-computed and stored in cache memory, so when a user request comes in, the matching process can quickly retrieve pre-prepared dimensional data rather than computing everything in real-time. This preliminary preparation significantly reduces the delay during actual coupon matching operations.
2Speed
If the system processes multi-dimensional data units in real-time without cache modules, then data accuracy is maintained, but network overhead increases and query speed decreases
Solution Approach 1:
The system performs preliminary actions by pre-processing dimensional data and storing it in cache modules before actual queries. The front-end processing device prepares dimensional filters in advance and caches them locally, eliminating the need to repeatedly query remote devices for the same dimensional data. This preliminary caching dramatically reduces network overhead and accelerates subsequent query operations.
Solution Approach 2:
The cache module acts as an intermediary between the front-end processing device and remote devices. Instead of direct communication between these components for every query, the cache module intercepts and satisfies queries using stored dimensional data, reducing network traffic. Only when cache data is insufficient or stale does the system communicate with remote devices, significantly lowering overall network overhead.
3Adaptability or versatility
If the system uses a simple single-dimensional matching approach, then the system complexity is low, but it fails to match multi-dimensional coupon data units effectively to complex user scenarios
Solution Approach 1:
The patent divides the complex multi-dimensional matching system into multiple independent processing modules, each responsible for a specific dimension (user information processing module, merchandise information processing module, channel information processing module, etc.). This segmentation allows the system to handle complex multi-dimensional matching tasks through simple, specialized components, making the overall complexity manageable while maintaining high adaptability.
Solution Approach 2:
The system introduces a new organizational dimension by structuring the matching process around multiple independent dimensions rather than a single flat structure. Each dimension adds a layer of filtering capability, transforming the matching process from a single-dimensional operation into a multi-dimensional filter chain. This dimensional approach enables effective matching of complex user scenarios while keeping individual processing modules relatively simple.
4Productivity
If the system dynamically adds or reduces matching modules based on user request thresholds, then resource utilization improves, but system stability may be affected by frequent configuration changes
Solution Approach 1:
The system implements dynamic configuration by allowing the number of matching modules to be adjusted based on user request thresholds. When traffic increases, additional modules are activated to handle the load; when traffic decreases, modules are reduced to conserve resources. This dynamic adaptation optimizes resource utilization while maintaining system functionality across varying load conditions.
Data Source
AI summary
A method and system for matching multi-dimensional data units in an electronic information system, the method comprising: arranging interface modules and cache modules on a front-end processing device, and one interface module and two or more cache modules forming a matching module; performing service processing for at least two dimensions on a remote device, and writing a processed multi-dimensional data unit into the cache module of the front-end processing device; and after the interface module receives a user request, determining dimension information corresponding to a current scene of the user request, inquiring a matched multi-dimensional data unit in the catch module of the matching module according to the dimension information, and sending the multi-dimensional data unit obtained by the query to an output module. The method and the system are capable of improving the efficiency and real-time performance of matching a multi-dimensional information scene to a multi-dimensional data unit.