Research and practice on spatio-temporal big data cloud platform of the belt and road initiative

Abstract

Spatio-temporal big data cloud platform is an important spatial information infrastructure that can provide different period spatial information data services, various spatial analysis services and flexible API services. Activities of policy coordination, facilities connectivity and unimpeded trade on the Belt and Road Initiative (B&R) will create huge demands to the spatial information infrastructure. This paper focuses on researching a distributed spatio-temporal big data engine and an extendable cloud platform framework suits for the B&R and some key technologies to implement them. A distributed spatio-temporal big data engine based on CassandraTM and an extendable 4-tier architecture cloud platform framework is put forward according to the spirit of parallel computing and cloud service. Four key technologies are discussed: 1) a storage and indexing method for distributed spatio-temporal big data, 2) an automatically collecting, processing, mapping and updating method of authoritative spatio-temporal data for web mapping service, 3) a schema of services aggregation based on nodes registering and services invoking based on view extension, 4) a distributed deployment and extension method of the cloud platform. We developed a distributed spatio-temporal big data center software and founded the main node platform portal with MapWorldTM map services and some thematic information services in China and built some local platform portals for those countries in the B&R area. The management and analysis services for spatiotemporal big data were built in flexible styles on this platform. Practices show that we provide a flexible and efficient solution to build the distributed spatio-temporal big data center and cloud platform, more node portals can be aggregated to the main portal by publishing their own web services and registering them in the aggregation schema. The data center and platform can support the storage and management of massive data well and has higher fault tolerance and better scalability.