Porcelain, as a significant component of traditional Chinese culture, carries a profound historical legacy and rich cultural connotations. Its study involves a complex knowledge system spanning multiple dynasties and regions. Traditional research methods often rely on documentary analysis and artifact examination, which may not fully reveal the artistic and cultural characteristics of porcelain. In recent years, the rapid development of digital technologies has presented new opportunities for the research, preservation, and dissemination of cultural heritage. Therefore, this paper leverages image processing techniques and large language models to conduct a multidimensional quantitative analysis of the artistic features of porcelain, employing scientific methods to investigate its artistic value. Additionally, we developed an interactive visualization system that enables users to comprehend the development of porcelain from a spatiotemporal perspective and engage in interactive exploration of its artistic features at both macro and micro levels. Case studies and user evaluations demonstrate the system’s high usability and efficiency, providing a novel academic perspective and tools for the in-depth research and digital dissemination of Chinese cultural heritage.

Big data processing clusters are suffering from a long job completion time due to the inefficient utilization of the RDMA capability. Our production measurement results in a large-scale cluster with hundreds of server nodes to process large-scale jobs have shown that the existing deployment of RDMA technique results in a long-tail job completion time, with some jobs even taking up more than twice the average time to complete. In this paper, we present the design and implementation of Turbo, an efficient communication framework for the large-scale data processing cluster to achieve high performance and scalability. The core of Turbo's approach is to leverage a dynamic block-level flowlet transmission mechanism and a non-blocking communication middleware to improve the network throughput and enhance system's scalability. Furthermore, Turbo ensures high system reliability by utilizing an external shuffle service as well as TCP serving as a backup. We integrate Turbo into Apache Spark and evaluate Turbo in a small-scale testbed and a large-scale cluster consisting of hundreds of server nodes. The small-scale testbed evaluation results show that Turbo improves the network throughput by 15.1% while maintaining high system reliability. The large-scale production results have shown Turbo can reduce the job completion time by 23.9% and increase the job completion rate by 2.03× over the existing RDMA solutions.