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ThinkMind // GEOProcessing 2016, The Eighth International Conference on Advanced Geographic Information Systems, Applications, and Services // View article geoprocessing_2016_9_20_30194


Spatial Data Supply Chain Provenance Modelling for Next Generation Spatial Infrastructures Using Semantic Web Technologies

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

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