Graph databases play a pivotal role in the FinTech industry. However, existing graph benchmarks fail to capture the unique characteristics of financial datasets and workloads, rendering them inadequate for evaluating graph databases in financial scenarios. This paper presents the LDBC Financial Benchmark (FinBench) Transaction Workload, a novel benchmark that adopts a choke point-driven design methodology, emphasizing performance bottlenecks, and incorporates distinct features such as dataset skewness, edge multiplicity, temporal window filtering, recursive path filtering, read-write query patterns, and truncation on hub vertices. Key contributions include a scalable data generator that synthesizes datasets with financial-specific features, a parameter generator that leverages bucketed data statistics for runtime consistency across queries, and a scalable benchmark driver that biases query execution by time windows. Experimental evaluations on graph databases demonstrate the benchmark's capability to reveal novel choke points and provide insights into system performance in financial scenarios.

The existing deep dehazing methods usually adopt end-to-end pixel-level optimization, generative adversarial networks or normalized flow methods, which would suffer several problems, e.g. smooth recovery results, unstable training and specific network structure design. Recently, the denoising diffusion probability model (DDPM) has shown great potential in image generation. Consequently, we propose a new DehazeDiff framework for image dehazing based on DDPM, aiming to explore a new solution. Firstly, a feature extraction network is introduced to extract the potential features from haze images to clear images, which are then fed into the diffusion model for further feature refinement. It can improve the learning ability of the model, and realize the haze removal. Secondly, by learning the noise distribution on the basis of the given hazy image and the clear image, the feature-guided conditional guidance is used in the image generation process to fully learn the input image features and improve the image quality. Experimental results on both indoor and outdoor datasets show that the proposed method outperforms the mainstream methods in terms of objective index and visual effect.