Model-Accuracy Aware Query Routing for Smart Logistics Service

Data driven business applications in logistics industry often issue prediction queries over relational databases to retrieve the newly generated transaction data for feature computations. In some cases even using slightly outdated data can result in significant inaccuracies in predictions. In another cases, we also observed the accuracy of model prediction is not sensitive to the data freshness. In the setting of primary-backup databases, one may choose to fetch the freshest data from the primary database to ensure model accuracy. However, this may hurt the performance of read-write transactions on the primary, especially when subjected to a high volume of prediction requests. In this work, we propose a Model-Accuracy Aware Service that facilitates a flexible trade-off between model prediction accuracy and primary database performance. This service implements an automated routing strategy aimed at minimizing the impact on the primary database's performance while meeting the requirements of model accuracy. It achieves this by leveraging the maintained database freshness information and the predictive results feedback from the model to learn the relationship between data discrepancy and prediction discrepancy in primary-backup scenarios. We report the experimental results on a real logistics application and also show its effectiveness on a public dataset.