NDLI: Finding corresponding objects when integrating several geo-spatial datasets

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Finding corresponding objects when integrating several geo-spatial datasets

Content Provider	ACM Digital Library
Author	Beeri, Catriel Kanza, Yaron Doytsher, Yerach Sagiv, Yehoshua Safra, Eliyahu
Abstract	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.
Starting Page	87
Ending Page	96
Page Count	10
File Format	PDF
ISBN	1595931465
DOI	10.1145/1097064.1097078
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-11-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Integration Spatial join Location-based join Corresponding objects Geospatial datasets
Content Type	Text
Resource Type	Article

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