# Automatically Expanding User-Management System for Massive Users in the Cloud Platform
## 1 Introduction  
Although the term \"Big Data\" is by no means a novelty in the field of aerospace engineering, there are many challenges that remain unsolved. A vision of data science, based on the activities of people who are \"learning from data\", was presented to improve that activity in an evidence-based manner. This new field is a better academic enlargement of statistics and machine learning than data science initiatives while being able to accommodate the same short-term goals [1]. As the data volume in aerospace engineering has dramatically increased in the past decade, the data-driven mode is often named the fourth paradigm. Especially for data-intensive science, data capture, curation, analysis, and sharing are facing new challenges [2]. Cloud computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Regardless of whether a cloud provider sells services at a low level or a higher level, cloud resources, including computing, storage, and networking, must all focus on the horizontal scalability of virtualized resources rather than on single-node performance [3]. A three-dimensional role-based user-management model is proposed. The three-dimensional role is defined as a vector composed of authority, scope, and permission time. This user-management model, based on the three-dimensional role, can satisfy the requirements of modern application systems and large-scale systems. Considering that users participate in an enterprise system as a particular identity, this results in roles of different users having different authorities, and each authority is valid within a relevant scope during its permission time [4]. In a geospatial hybrid cloud platform based on multi-sourced computing and model resources, a user-management module was proposed to manage the accounts for different types of end users. There were several categories of users, including cloud resource contributors, cloud resource consumers, and cloud administrators, to be managed in this user-management module [5]. Some researchers designed an infrastructure of multiple data centers (MDCs) for managing and processing massive remote-sensing images by introducing access security and information services. Based on this MDC, they succeeded in resolving problems regarding procedures in processing applications collaboratively and transferring massive remote-sensing datasets quickly, with stable cross-MDC with the aid of a data center user-management system [6; 7]. Although the conception and application of big data processing have been presented and practiced, user-management systems across distributed data centers or cloud platform nodes have rarely been mentioned. Especially with the cloud computing technology being put into use for updating the elastic supporting capacity of the cloud platform, this paper proposes an architecture and functional design of the remote-sensing cloud service platform and introduces a prototype. This remote-sensing cloud service prototype allows users to choose required remote-sensing data and software and automatically deploys them to a virtual computer that users can access through the Internet to perform their remote-sensing data processing and application. Experiments show that the remote-sensing cloud service platform can gather remote-sensing information, software, and computing resources from different providers and provide them for sharing on user's demand [8]. By virtue of its excellent scalability and high reliability, the cloud computing system has been introduced to provide significant technical support for improving the service capability of the cloud platform. A hierarchical framework was proposed that allows for an integrated and homogeneous management of heterogeneous platforms but, at the same time, preserves the autonomy of single sites.