  In this paper, we investigate a novel spatial dataset search paradigm over
multiple spatial data sources, which enables users to conduct join and union
searches seamlessly. Specifically, we define two search problems called Maximum
Intersection Query (MIQ) and Maximum Coverage Query with a Connection
constraint (MCQC). To address these problems, we propose a unified Multi-source
Spatial Dataset Search (MSDS) framework. In MSDS, we design a multi-layer index
to accelerate the MIQ and MCQC. In addition, we prove that the MCQC is NP-hard
and design two greedy algorithms to solve the problem. To deal with the
constant update of spatial datasets in each data source, we design a dynamic
index updating strategy and optimize search algorithms to reduce communication
costs and improve search efficiency. We evaluate the efficiency of MSDS on five
real-world data sources, and the experimental results show that our framework
is able to achieve a significant reduction in running time and communication
cost.
