Stateless Processor Pipeline for Heterogeneous Product Data Normalization
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Solution Overview
Problem
Current online storefronts lack a flexible and scalable mechanism for deriving and normalizing product recommendations from multiple heterogeneous sources, failing to provide a comprehensive and relevant shopping experience for customers.
Innovation Solution
A system and method that utilizes a pipeline of stateless processors to normalize and refine product recommendations from diverse sources, transforming them into a unified format suitable for display on a virtual storefront, allowing for parallel processing and auto-provisioning to handle multiple requests efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If product recommendations are derived from multiple heterogeneous sources, then the variety and completeness of product catalog is improved, but the complexity of data normalization and refinement increases
Solution Approach 1:
The system segments the complex data normalization task into multiple independent stateless processors that handle specific aspects of product data (e.g., price normalization, availability checking, category mapping). Each processor handles a discrete function, making the overall complex system manageable and maintainable while supporting multiple heterogeneous sources.
Solution Approach 2:
The patent introduces intermediary components including an adapter layer that connects heterogeneous sources to the normalization pipeline, and a product database that serves as an intermediary repository between raw data and finalized recommendations. These intermediaries buffer and standardize data flow, reducing the complexity of direct integration between diverse sources and the recommendation engine.
2Productivity
If a pipeline of stateless processors is used for normalization, then the system scalability is improved, but the processing time for each product recommendation increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and normalizing product data from heterogeneous sources before it reaches the recommendation engine. The stateless processors prepare data in advance, creating a ready-to-use normalized product database that can be quickly queried during recommendation generation, thus reducing real-time processing delays.
Solution Approach 2:
The patent implements continuous data normalization and refinement through the pipeline of stateless processors, which operate continuously to maintain an up-to-date normalized product catalog. This continuous processing ensures that recommendation queries can be served quickly from pre-normalized data without requiring repeated full-processing cycles.
Data Source
AI summary
Methods and Systems for displaying product recommendations at a virtual storefront by receiving product recommendations from a plurality of heterogeneous sources. The source servers send the product recommendations from their end that are then normalized and refined by a recommendation engine based on a plurality of factors ensuring that the products are presentable and sellable on the storefront. The recommendation collation is accomplished using a pipeline of stateless processors, thereby providing a highly scalable platform perfectly suited for cloud-based computational platforms.