The advent of geospatial big data has led to a paradigm shift where most related applications became data driven, and therefore intensive in both data and computation. This revolution has covered most domains, namely the real-time systems such as web search engines, social networks, and tracking systems. These later are linked to the high-velocity feature, which characterizes the dynamism, the fast changing and moving data streams. Therefore, the response time and speed of such queries, along with the space complexity, are among data stream analysis system requirements, which still require improvements using sophisticated algorithms. In this vein, this chapter discusses new approaches that can reduce the complexity and costs in time and space while improving the efficiency and quality of responses of geospatial big data stream analysis to efficiently detect changes over time, conclude, and predict future events.
Deep Data Stream Analysis Model and Algorithm With Memory Mechanism
