Database Search Indexing via Seasonality and Newness Prediction
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Solution Overview
Problem
E-commerce database systems face performance issues due to increased latency as the number of products grows, leading to slow search engine performance, and existing strategies for dividing items into index groups result in high Type-II errors, where popular items are misclassified as 'cold', causing longer latency and decreased conversion rates.
Innovation Solution
Implementing a prioritized product index structure using a supervised machine learning model that predicts item popularity based on historical access data, incorporating seasonality and newness factors, and adjusting the hot/cold database cluster allocation to minimize false negatives, with a cost-sensitive algorithm to optimize error costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of products in the database system increases, then the variety of products available for purchase increases, but the performance of the database system slows down due to increased latency
Solution Approach 1:
The patent divides the database system into multiple database clusters (hot cluster and cold cluster) to segment the workload. Frequently accessed products are placed in the hot cluster for fast access, while less frequently accessed products are placed in the cold cluster. This segmentation allows the system to handle a larger total number of products while maintaining fast access performance for popular items.
2Ease of operation
If existing strategies are used to divide items into index groups, then the database can be organized for access, but Type-II errors increase where popular items are misclassified as cold, causing longer latency and decreased conversion rates
Solution Approach 1:
The patent implements a feedback mechanism that monitors access patterns and uses this information to dynamically adjust the classification of products between hot and cold clusters. By continuously observing which products are frequently accessed and adjusting the classification accordingly, the system reduces Type-II errors where popular items are misclassified as cold, thereby maintaining high classification accuracy and fast access performance.
Data Source
AI summary
Various embodiments can comprise a system, which can comprise one or more processing modules; and/or one or more non-transitory memory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: receiving a request from a requesting party to produce a feature vector for a record, the feature vector being used to aid in placing the record in one of a first database cluster H or a second database cluster L; retrieving a first access history for the record at a first time period, the first access history indicating a number of times the record was accessed within the first time period that is a predetermined time period away from a current day; repeating retrieving one or more second access histories for the record for one or more additional time periods, each second access history of the one or more second access histories indicating a number of times the record was accessed within the one or more additional time periods, wherein each of the one or more additional time periods are different predetermined time periods away from the current day; generating the feature vector based on the first access history and the one or more second access histories of the record; and sending the feature vector to the requesting party. Other embodiments are disclosed herein.