An Adaptive Multilevel Indexing Method for Disaster Service Discovery

With the globe facing various scales of natural disasters then and there, disaster recovery is one among the hottest research areas and the rescue and recovery services can be highly benefitted with the advancements of information and communications technology (ICT). Enhanced rescue effect can be achieved through the dynamic networking of people, systems and procedures. A seamless integration of these elements along with the service-oriented systems can satisfy the mission objectives with the maximum effect. In disaster management systems, services from multiple sources are usually integrated and composed into a usable format in order to effectively drive the decision-making process. Therefore, a novel service indexing method is required to effectively discover desirable services from the large-scale disaster service repositories, comprising a huge number of services. With this in mind, this paper presents a novel multilevel indexing algorithm based on the equivalence theory in order to achieve effective service discovery in large-scale disaster service repositories. The performance and efficiency of the proposed model have been evaluated by both theoretical analysis and practical experiments. The experimental results proved that the proposed algorithm is more efficient for service discovery and composition than existing inverted index methods.