Toward integrating grid and cloud-based concepts for an enhanced deployment of spatial data warehouses in cyber-physical system applications

Thanks to their spatially distributed sensors, cyber-physical system (CPS) applications are currently collecting large amounts of heterogeneous data. When it comes to allowing several decision-makers to collaboratively plan their actions, these applications need appropriate tools for an efficient storage, analysis, and visualization of the available data. Spatial data warehouses (SDWs) have proven their efficiency in carrying out these operations. However, because of the increasing volumes of data, the commonly used spatial extract-transform-load (SETL) process generally fails to update the SDW within acceptable timeframes. In order to solve this problem, we propose to perform the SETL tasks in a distributed, parallel manner by means of a grid of computing resources. In addition to being the unique solution that uses grid computing for the SETL process of SDWs, our solution makes use of cloud computing techniques to shorten the spatial data processing time and reduce resource consumption. To meet our goals, we propose a multi-agent-based solution to adequately schedule and balance the processing activities over the grid while allowing a joint use of real-time and archive data for personalized reporting and visualization of services envisioned to the decision-makers who are using the same CPS application.