WAVES as Big Data Platform for Real-time Semantic Stream Management

In most parts of the world, fast growing urbanization has faced several challenges throughout the last decades. Achieving a sustainable, environment-friendly and highly operating cities for a better quality of life requires innovative solutions brought by cutting edge technology. Creative ways of thinking have opened a brand new world of possibilities such as smart grids, smarter buildings and smart transportation systems. Most of these systems exploit sensor networks, an important component of the Internet Of Things, forcing to provide spatio-temporal processing that shares some characteristics with Big Data ecosystems. Indeed processing streaming sensor data requires technologies to face the high volume of data and the velocity at which these data ara coming into the system. Considering these challenges, WAVES provides real-time platform for processing sensor data in general, where it takes advantage of the flexibility and depth afforded by Semantic Web technologies. The primary use case is to deal with smart water network management and detect in real-time anoamlies based on reasoning capabilities. With its abstract leved design, it can cover other various domains where sensor networks are exploited such as traffic control, power consumption and e-Commerce. The following generic architecture of WAVES depicts the main different components to process structured and unstructured streaming sensor data on the fly. The key idea is to develop a comprehensive generic software platform that allows the management of large volumes of data streams. The platform has the ability to collect data streams from various sources in order to create new insights and knowledge for users accessed through innovative information and following some processing steps (cleaning, pre-processing, interlinking, filtering, summary and visualization).