Collaborative information analysis for sensor-enabled scientific applications

Data collected from sensor networks are often analysed by cross-domain scientists who produce results that are requested by a variety of clients. In such a collaborative environment, scientific experiments include data collection form sensors, and data analysis performed by scientists. To meet the client requirements these activities have to be dynamically coordinated. Furthermore, this coordination must occur whenever data analysis results indicate that sensor data streams need to be adjusted to provide desirable results. In this paper, we present a platform and the design of its architecture that enable such real-time collaborative analysis of sensor data. We also discuss a case study from plant phenomics research. We illustrate that our solution permits scientists to build executable data models and conduct immediate data analysis that are driven by direct feedback from clients.