Spatial crowdsourcing platforms have become indispensable in addressing the evolving needs of modern society. These platforms facilitate essential services such as ride-sharing, on-demand food delivery, and efficient parcel distribution. However, the uneven distribution of workers and requests under a single-platform setting may lead to the loss of tasks. To address this issue, we introduce the Cross Online Matching (COM) problem, which facilitates collaboration among multiple platforms. We first propose DemCOM and RamCOM, which adopt deterministic greedy and randomized trade-off strategies, respectively. Furthermore, we develop a Utility-Distribution Aware Cooperative Online Matching (UDACOM) algorithm that leverages supply-demand relationships to optimize decision-making. Theoretical analysis confirms the competitive ratios of our algorithms. Validated on both real and synthetic datasets, our approach significantly outperforms state-of-the-art methods, achieving a 5% increase in total revenue and a 3% improvement in the successful matching rate.

Abstract In recent years, the task of knowledge graph completion has attracted significant attention from researchers. In practical scenarios, multi-source knowledge graph completion is quite common. Federated knowledge graph embedding enables joint learning across multiple knowledge graphs while ensuring data privacy and security. Generally, each data source has a different data distribution. They may exhibit various connections, such as combinatorial, hierarchical, and symmetric/asymmetric connections. Existing federated knowledge graph models overlook the data heterogeneity of knowledge graphs from different sources, using a unified scoring function to assess the quality of the generated embedding vectors from different clients. This limitation affects the quality of knowledge graph embeddings generated by each client. Therefore, this paper proposes a federated knowledge graph embedding framework based on the diffusion model. On the client side, we employ diffusion model to learn knowledge graph embeddings. We utilize the diffusion model's forward noise-adding process to learn the knowledge graph's distribution. We then use the reverse denoising process to generate knowledge embeddings directly. Additionally, we employ knowledge distillation during client model training to address the drift between local optimization and global convergence. Since the original data cannot leave the local environment in federated learning, we adopt a framework that shares diffusion models for federated knowledge graph completion. Extensive experiments demonstrate that our model significantly outperforms existing state-of-the-art methods in three benchmark datasets.

Spatial platforms have become increasingly important in people's daily lives. Task assignment is a critical problem in these platforms that matches real-time orders to suitable workers. Most studies only focus on independent platforms that are in a competitive relationship. Recently, an emerging service model was proposed, where orders are shared with multiple similar platforms. It aims to solve the imbalance between supply and demand through cooperation. However, it faces the following main challenges: 1) Coordinating independent platforms fairly based on the limited information; 2) Building a task assignment process with personalized algorithms. In this paper, we study real applications and define the Autonomy and Coordination Task Assignment problem (ACTA) to maximize the global revenue and fairness. We propose a framework to solve ACTA that consists of public order sending, local matching, global conflict adjustment and results notification. The framework uses mid-products and public data to train a revenue estimation model to coordinate participants. We further propose dynamic weight task assignment algorithms to guarantee fairness. Through the experiments, we prove that the platforms can obtain higher revenue, which shows the effectiveness and efficiency of our work.

Online spatial crowdsourcing platforms provide popular O2O services in people's daily. Users submit real-time tasks through the Internet and require the platform to immediately assign workers to serve them. However, the imbalance distribution of tasks and workers leads to the rejection of some tasks, which reduces the profit of the platform. In this paper, we propose that similar platforms can form an alliance to make full use of the global service supply through cooperation. We name the problem asGlobalTaskAssignment (GTA), in which platforms are allowed to hire idle workers from other platforms to improve the profit of all the platforms together. Different from relevant works, the decision-makers in GTA are platforms rather than individual workers, which can better assign workers in all platforms and improve the overall profit. We design an auction-based incentive mechanism (AIM), to motivate platforms to rent idle workers to other platforms so that increase their own profit. Based on the mechanism, we propose a greedy-based assignment algorithm (BaseGTA), in which platforms greedily maximizes their current profit. We further propose a prediction-based assignment algorithm (ImpGTA), in which platforms make decisions based on the spatial-temporal distribution in the future time. Experimental results show that platforms using our algorithms can achieve higher profit than the existing studies.

Real-life graph datasets extracted from the Web are inevitably full of incompleteness, conflicts, and redundancies, so graph data cleaning shows its necessity. Although rules like data dependencies have been widely studied in relational data repairing, very few works exist to repair graph data. In this article, we introduce a repairing semantics for graphs, calledGraph-Repairing Rules(${\sf GRR}$s). This semantics can capture the incompleteness, conflicts, and redundancies in graphs and indicate how to correct these errors. However, this graph repairing semantics can only repair the graphs strictly isomorphic to the rule patterns, which decreases the utility of the rules. To overcome this shortcoming, we further propose a flexible rule-based graph repairing semantics (called$\delta$-GRR). We study three fundamental problems associated with both${\sf GRR}$s and$\delta$-GRRs, consistency, implication, and termination, which show whether a given set of rules make sense. Repairing the graph data using${\sf GRR}$s or$\delta$-GRRs involves a problem of finding isomorphic subgraphs of the graph data, which is NP-complete. To efficiently circumvent the complex calculation of subgraph isomorphism, we design a decomposition-and-join strategy to solve this problem. Extensive experiments on real datasets show that our two graph repairing semantics and corresponding repairing algorithms can effectively and efficiently repair real-life graph data.

In recent years, online Event Based Social Network (EBSN) platforms have become increasingly popular. One typical task of EBSN platforms is to help users make suitable and personalized plans for participating in different interesting social events. Existing techniques either ignore the minimum-participant requirement constraint for each event, which is crucially needed for some events to be held successfully, or assume that events would not change once announced. In this paper, we address the above inadequacies of existing EBSN techniques. We formally define the Global Event Planning with Constraints (GEPC) problem, and its incremental variant. Since these problems are NP-hard, and provide approximate solutions. Finally, we verify the effectiveness and efficiency of our proposed algorithms through extensive experiments over real and synthetic datasets.

With the development of mobile communication techniques, spatial crowdsourcing has become popular recently. A typical topic of spatial crowdsourcing is task assignment, which assigns crowd workers to users' requests in real time and maximizes the total revenue. However, it is common that the available crowd workers over a platform are too far away to serve the requests, so some user requests may be rejected or responded at high money cost after long waiting. Fortunately, the neighbors of a platform usually have available resources for the same services. Collaboratively conducting the task allocation among different platforms can greatly improve the quality of services, but have not been investigated yet. In this paper, we propose a Cross Online Matching (COM), which enables a platform to "borrow" unoccupied crowd workers from other platforms for completing the user requests. We propose two algorithms, deterministic cross online matching (DemCOM) and randomized cross online matching (RamCom) for COM. DemCOM focuses on the largest obtained revenue in a greedy manner, while RamCom considers the trade-off between the obtained revenue and the probability of request being accepted by the borrowed workers. Extensive experimental results verify the effectiveness and efficiency of our algorithms.

The popularity of mobile Internet techniques and Online-To-Offline(O2O) business models has led to the emergence of various spatial crowdsourcing (SC) platforms in our daily life. A core issue of SC platforms is to assign tasks to suitable crowd workers. Existing approaches usually focus on the matching of two types of objects,tasks and workers, and let workers to travel to the location of usersto provide services, which is a 2D matching problem. However, recent services provided by some new platforms, such as person-alized haircut service1and station ride-sharing, need users andworkers travel together to a third workplace to complete the service, which is indeed a 3D matching problem. Approaches in the existingstudies either cannot solve such 3D matching problem, or lack aassignment plan satisfying both users' and workers' preference inreal applications. Thus, in this paper, we propose a 3-Dimensional Stable Spatial Matching(3D-SSM) for the 3D matching problem innew SC services. We prove that the 3D-SSM problem is NP-hard, and propose two baseline algorithms and two efficient approximatealgorithms with bounded approximate ratios to solve it. Finally, weconduct extensive experiment studies which verify the efficiencyand effectiveness of the proposed algorithms on real and synthetic datasets.