A Two-Stage Framework for Big Spatial Data Analytics to Support Disaster Response

During disaster response, large volumes and diverse types of data sets are often continuously generated, and in many case these data sets create overwhelming burdens to data processing infrastructure and teams. At the same time, decision-making during disaster response requires timely and relevant information which has to be extracted as expeditiously as possible from these large data sets. Therefore, processing of disaster related data sets is often time sensitive and requires coordination and prioritization. To accomplish this, we propose a two-stage approach to facilitate efficient and effective data processing for disaster decision support. In the first stage, a Data Envelope Analysis (DEA) model is introduced to model the articulation process about information needs such that providing a formal way of prioritizing data processing task. In the second stage, the prioritized data processing workflow is implemented on an Apache Storm based streaming processing platform in the EC2 cloud, with a focus on computational resource optimization. To validate the proposed approach, a Hurricane Sandy based use case was used to evaluate the performance of the proposed approach. Results show that our approach can compute up to 69% (three supervisor nodes) faster than a conventional serial processing approach.