An architecture for detecting events in real-time using massive heterogeneous data sources

The wealth of information that is readily available nowadays grants researchers and practitioners the ability to develop techniques and applications that monitor and react to all sorts of circumstances: from network congestions to natural catastrophies. Therefore, it is no longer a question of whether this can be done, but how to do it in real-time, and if possible proactively. Consequently, it becomes a necessity to develop a platform that will aggregate all the necessary information and will orchestrate it in the best way possible, towards meeting these goals. A main problem that arises in such a setting is the high diversity of the incoming data, obtained from very different sources such as sensors, smart phones, GPS signals and social networks. The large volume of the incoming data is a gift that ensures high quality of the produced output, but also a curse, because higher computational resources are needed. In this paper, we present the architecture of a framework designed to gather, aggregate and process a wide range of sensory input coming from very different sources. A distinctive characteristic of our framework is the active involvement of citizens. We guide the description of how our framework meets our requirements through two indicative use cases.