In multisite data hub environments, data transfer costs and resource constraints challenge efficient data analysis workflow execution. Traditional analysis methods across multi-hubs often reduce performance and increase costs. To address these challenges, we introduce CROSS (Collaborative Resource-Oriented Scheduling System), which optimizes distributed workflows by utilizing data and resources across multiple hubs. CROSS enables collaboration between hubs, minimizes data transfer, and maximizes resource use through an efficient scheduling algorithm that considers data locality, resources, and workflow structure. Experiments with four scientific workflows show CROSS reduces makespan by up to 33.5% and improves CPU and memory efficiency by 1.58x and 1.59x, respectively, making it effective for multisite workflows.

With the growing applications of big data and artificial intelligence, the quality of the service is an outcome of the quality of the data. Nevertheless, there is still a significant lack of data that has practical application value. To solve these problems, we propose SODAS (Smart Open Data As a Service) as a novel open data platform for efficient data sharing and utilization. We first analyze the major problems in the legacy CKAN and then draw up their solutions through core strategies. We next define four components and nine function blocks of SODAS for each core strategy. As a result, SODAS drives Open Data Portal, Open Data Reference Model, DataMap Publisher, and ADE Provisioning (Analytics and Development Environment Provisioning) by connecting the defined function blocks. We confirm that each function works correctly through the SODAS Web portal and apply SODAS to actual data distribution sites to prove its efficiency and practical use. SODAS is the first open data platform that provides secure interoperability between heterogeneous platforms based on international standards and enables domain-free data management with flexible metadata.