We address the problem of processing graph pattern matching queries over a massive set of data graphs in this letter. As the number of data graphs is growing rapidly, it is often hard to process such queries with serial algorithms in a timely manner. We propose a distributed graph querying algorithm, which employs feature-based comparison and a filter-and-verify scheme working on the MapReduce framework. Moreover, we devise an efficient scheme that adaptively tunes a proper feature size at runtime by sampling data graphs. With various experiments, we show that the proposed method outperforms conventional algorithms in terms of scalability and efficiency.

The design of automobile lamps requires accurate estimation of heat distribution to prevent overheating and deformation of the product. Traditional heat resistant analysis using Computational Fluid Dynamics (CFD) is time-consuming and requires expertise in thermofluid mechanics, making real-time temperature analysis less accessible to lamp designers. We propose a machine learning-based temperature prediction system for automobile lamp design. We trained our machine learning models using CFD results of various lamp designs, providing lamp designers real-time Heat-Resistant Analysis. Comprehensive tests on real lamp products demonstrate that our prediction model accurately estimates heat distribution comparable to CFD analysis within a minute. Our system visualizes the estimated heat distribution of car lamp design supporting quick decision-making by lamp designer. It is expected to shorten the product design process, improving the market competitiveness.