An Efficient Machine-Generated Data Modeling Approach Based on Domain-Aware Knowledge for Intelligent Consumer Electronics

Abstract

Data modeling is a critical component in the development of next-generation consumer electronics (CE), as it provides massive data support to implement smart services related to CE products, such as complex correlation analysis of the CE market, the development of technology, and consumer behaviors. However, the existing works still face the following challenges: (1) the manual data processing is time-consuming and labor-intensive while analyzing the massive data with domain knowledge; (2) insufficient attention is given to the analysis of relationships among data collections; and (3) redundant data is not eliminated, leading to excess computational burden. To address these challenges, we propose an efficient automatic data modeling method that employs domain-aware knowledge, significantly reducing the cost of data modeling. Our approach starts by leveraging a knowledge-based classifier to extract domain-related resources from open common single-document summarization datasets (OC-SDS), thus reducing data acquisition expenses. Then after collecting related documents by original summaries, a two-stage filtering process is applied to eliminate redundant and non-related documents. Finally, the original summaries are iteratively updated until reaching the threshold, enhancing informativeness and introducing novel expressions without the need for manual marking. As a practical application, we take emergency news as an example and built a typical dataset based on our method. After an extensive analysis, the empirical results evidence that our method is superior in scale and quality compared to the existing methods, with over 13.7% more related data, providing a valuable contribution to the field of CE product development.