This cutting-edge reference outlines the underlying theory and principles of efficient and effective distributed pattern recognition involving one-shot learning and in-network processing for different types of applications, including multimedia retrieval systems and event detection over different network environments. Investigating one-shot learning and in-network processing as complementary mechanisms for efficient and accurate distributed pattern analyses, it presents the technical aspects related to the development of scalable pattern recognition using a number of contemporary application development tools. It also considers scalability of pattern recognition schemes when dealing with such data.