Graph entity dependencies (GEDs) are novel graph constraints, unifying keys and functional dependencies, for property graphs. They have been found useful in many real-world data quality and data management tasks, including fact checking on social media networks and entity resolution. In this paper, we study the discovery problem of GEDs—finding a minimal cover of valid GEDs in a given graph data. We formalise the problem, and propose an effective and efficient approach to overcome major bottlenecks in GED discovery. In particular, we leverage existing graph partitioning algorithms to enable fast GED-scope discovery, and employ effective pruning strategies over the prohibitively large space of candidate dependencies. Furthermore, we define an interestingness measure for GEDs based on the minimum description length principle, to score and rank the mined cover set of GEDs. Finally, we demonstrate the scalability and effectiveness of our GED discovery approach through extensive experiments on real-world benchmark graph data sets; and present the usefulness of the discovered rules in different downstream data quality management applications.

As a promising battery-sharing service, battery swapping has become one of the most important electric-scooter (called e-scooter) energy refueling mechanisms. However, by analyzing a battery swapping dataset of 108,574 e-scooter drivers and 41,358 batteries and surveying 224 of these drivers, we find that drivers are facing insufficient energy replenishment during the battery swapping process, i.e., swapping out an undercharged battery from the battery swapping station. To identify the root cause, we perform a data-driven analysis and reveal an imbalance in spatial and temporal swapping behavior patterns, which causes a significant decrease in battery charging time. Inspired by these findings, we design iSwap, a novel spatio-temporal battery swapping recommendation system, which improves drivers’ swapping experience by proactively coordinating spatio-temporal unbalanced behavior patterns. iSwap considers not only drivers’ individual temporal preference for swapping time recommendation (i.e., when to swap) but also the complex mutual influence among drivers for swapping station recommendation (i.e., where to swap). To perform the two tasks efficiently, we propose a swapping behavior-aware phase demand balancing schema and a submodularity-based near-optimal swapping station recommendation algorithm. Finally, through the evaluation, iSwap improves the probability of swapping out a fully charged battery by 42.1% and driver satisfaction by 16.8% compared to the ground truth.

Abstract Lung segmentation is an essential step in a computer‐aided diagnosis system for chest radiographs. The lung parenchyma is first segmented in pulmonary computer‐aided diagnosis systems to remove the interference of non‐lung regions while increasing the effectiveness of the subsequent work. Nevertheless, most medical image segmentation methods nowadays use U‐Net and its variants. These variant networks perform poorly in segmentation to detect smaller structures and cannot accurately segment boundary regions. A multi‐interaction feature fusion network model based on Kiu‐Net is presented in this paper to address this problem. Specifically, U‐Net and Ki‐Net are first utilized to extract high‐level and detailed features of chest images, respectively. Then, cross‐residual fusion modules are employed in the network encoding stage to obtain complementary features from these two networks. Second, the global information module is introduced to guarantee the segmented region's integrity. Finally, in the network decoding stage, the multi‐interaction module is presented, which allows to interact with multiple kinds of information, such as global contextual information, branching features, and fused features, to obtain more practical information. The performance of the proposed model was assessed on both the Montgomery County (MC) and Shenzhen datasets, demonstrating its superiority over existing methods according to the experimental results.