Home // GEOProcessing 2016, The Eighth International Conference on Advanced Geographic Information Systems, Applications, and Services // View article
Authors:
Muhammad Azeem Sadiq
David McMeekin
Lesley Arnold
Keywords: spatial data supply chain; spatuial data provenance; semantic Web; ontology; trust; processess and services
Abstract:
This research addresses spatial data supply chain provenance issues using semantic Web technologies to resolve knowledge gaps when disseminating spatial data products. Two models from the World Wide Web Consortium (W3C) and the Open Provenance Group for general data on the Web do not satisfy geospatial end-user needs. The Open Geospatial Consortium (OGC) has investigated the W3C PROV model for spatial datasets. Issues identified are the lack of provenance captured at the feature and attribute level, and for time series, data set series, representation and presentation interfaces, and elements at different levels. In order to answer user queries comprehensively, a geospatial provenance model in conjunction with semantic technologies has been identified as a potential solution to increase a user’s trust in datasets and processes. This is important as raster dataset provenance, time series conflation processes and incremental updates have not been addressed. This has created a critical gap between provenance currency and the believability of geospatial datasets.
Pages: 146 to 153
Copyright: Copyright (c) IARIA, 2016
Publication date: April 24, 2016
Published in: conference
ISSN: 2308-393X
ISBN: 978-1-61208-469-5
Location: Venice, Italy
Dates: from April 24, 2016 to April 28, 2016