Searching moving objects in a spatio-temporal distributed database servers system

(querying about the time-varying locations of moving objects is particularly cumbersome in environments composed of a very large number of distributed spatio-temporal database servers. In particular, searching for a specific object can require to visit each server. In this paper we propose a strategy to avoid such an exhaustive search that is based on the use of a centralized index, called meta-index, which is the entry point for spatio-temporal search queries. This index allows a software agent to determine a search plan for visiting the most likely servers to contain the target object. An important issue for large and dynamic distributed servers systems is to keep the meta-index as up-to-date as possible with the real system. This paper defines and compares two different strategies for maintaining properly updated the meta-index: crawling, where the centralized system that keeps the index controls itself the updating process, and harvesting, where each distributed database server autonomously transfers data directly into the central index system. Both strategies were implemented and compared by using discrete-event simulators with demanding synthetic spatio-temporal data. The results show that crawling has better performance.