Finding corresponding objects when integrating several geo-spatial datasets

When integrating geo-spatial datasets, a join algorithm is used for finding sets of corresponding objects (i.e., objects that represent the same real-world entity). Algorithms for joining two datasets were studied in the past. This paper investigates integration of three datasets and proposes methods that can be easily generalized to any number of datasets. Two approaches that use only locations of objects are presented and compared. In one approach, a join algorithm for two datasets is applied sequentially. In the second approach, all the integrated datasets are processed simultaneously. For the two approaches, join algorithms are given and their performances, in terms of recall and precision, are compared. The algorithms are designed to perform well even when locations are imprecise and each dataset represents only some of the real-world entities. Results of extensive experiments show that one of the algorithms has the best (or close to the best) performances under all circumstances. This algorithm has a much better performance than applying sequentially the one-sided nearest-neighbor join.