SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment
This work addresses the model selection problem in big data applications, particularly for traffic scenarios, but it appears incremental as it builds on existing assignment methods by focusing on scenario-dataset-model assignments.
The paper tackles the challenge of selecting the optimal model for specific scenarios by introducing the Scenario-based Optimal Model Assignment (SOMA) problem and developing the SMAP framework, which integrates heterogeneous information and uses a multi-head attention score function to achieve effective model selection, as validated through experiments on six traffic scenarios.
The increasing maturity of big data applications has led to a proliferation of models targeting the same objectives within the same scenarios and datasets. However, selecting the most suitable model that considers model's features while taking specific requirements and constraints into account still poses a significant challenge. Existing methods have focused on worker-task assignments based on crowdsourcing, they neglect the scenario-dataset-model assignment problem. To address this challenge, a new problem named the Scenario-based Optimal Model Assignment (SOMA) problem is introduced and a novel framework entitled Scenario and Model Associative percepts (SMAP) is developed. SMAP is a heterogeneous information framework that can integrate various types of information to intelligently select a suitable dataset and allocate the optimal model for a specific scenario. To comprehensively evaluate models, a new score function that utilizes multi-head attention mechanisms is proposed. Moreover, a novel memory mechanism named the mnemonic center is developed to store the matched heterogeneous information and prevent duplicate matching. Six popular traffic scenarios are selected as study cases and extensive experiments are conducted on a dataset to verify the effectiveness and efficiency of SMAP and the score function.