Semantically Augmented Range Queries over Heterogeneous Geospatial Data
Authors: 
Goce Trajcevski, Booma Sowkarthiga Balasubramani, Isabel F. Cruz, 
Roberto Tamassia, Xu TengAuthors Info & Claims
Pages 68 - 77
Abstract
Geospatial data integration combines two or more data layers to facilitate advanced querying, analysis, reasoning, and visualization. In general, different layers (e.g., ZIP codes, census blocks, school districts, and land use parcels) have different spatial partitions and different types of associated semantic descriptors. In addition, geospatial data may contain errors (e.g., due to imprecision in the measurements or to representation constraints) causing uncertainty that needs to be incorporated and quantified in the query answers. In this paper, we leverage semantic descriptors in heterogeneous information layers to build a data structure that enables efficient processing of geospatial range queries by returning an estimate of the answer together with an error bound. We present the processing algorithms and evaluate our approach by means of experiments that encompass large datasets, demonstrating the benefits of our approach.
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- Semantically Augmented Range Queries over Heterogeneous Geospatial Data
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Published In
November 2020
687 pages
ISBN:9781450380195
DOI:10.1145/3397536
Copyright © 2020 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Published: 13 November 2020
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