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















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. Based on the recent developments, WAVES platform deploys an abstract level design taking advantages of the flexibility and depth afforded by Semantic Web technologies. It covers various domains where sensor networks are exploited such as traffic control, power consumption and e-Commerce.

The key idea is to develop a comprehensive generic, modular and distributed platform that allows the management of large volumes of data streams. The platform has the ability to collect data from various sources such as Linked Data and sensor networks, in order to create new insights and knowledge for users accessed through innovative information and following several processing steps: (1) Data cleaning and pre-processing; (2) Semantic Filtering and Summarizing; (3) Reasoning and anomaly detection; (4) Visualization.

WAVES exploits Linked Data to enrich sensor data with contextual information and thus enhance reasoning capabilities. Context has been particularly proven to be essential for accurate and robust anomaly detection. What appears to be anomalous in sensor network may be found to be false positives when introducing context. The primary use in WAVES is to deal with smart water network management and detect anomalies in a real-time environment. The application can also answer all economic actors in need for an effective management of their own streaming data and those produced in their field of activity by others (partners, suppliers, customers, organizations, agencies, etc.). For further details, please refer to http://waves-rsp.org/ which provides the list of scientific papers and technical reports published in this project.


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