Dynamic Data Driven Application Systems (DDDAS) for Multimedia Content Analysis

Abstract

With ubiquitous data acquired from sensors, there is an ever increasing ability to abstract content from the environment. Multimedia content exists in many data forms such as surveillance data from video, reports from documents and twitter, and signals from systems. Current discussions revolve around dynamic data-driven applications systems (DDDAS), big data, cyber-physical systems, and Internet of things (IoT); each of which requires data modeling. Key elements include a computing environment that should match the application, time horizon, and queries for which the data is needed. In this chapter, we discuss the DDDAS paradigm of sensor measurements, statistical processing, environmental modeling, and software implementation to deliver content on demand, given the context of the environment. DDDAS provides a framework to control the information flow for rapid decision making, model updating, and being prepared for the unexpected query. Experimental results demonstrate the DDDAS-based Live Video Computing DataBase Modeling approach to allow data discovery, model updates, and query-based flexibility for awareness of unknown situations.