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
, 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:
, where the centralized system that keeps the index controls itself the updating process, and
, 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.