Enhancing discovery in spatial data infrastructures using a search engine

Paolo Corti

doi:10.7717/peerj-cs.152

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Enhancing discovery in spatial data infrastructures using a search engine

2018-05-21

Paolo Corti *

Athanasios Tom Kralidis

Benjamin Lewis

DOI：10.7717/peerj-cs.152

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Abstract

En 中文

A spatial data infrastructure (SDI) is a framework of geospatial data, metadata, users and tools intended to provide an efficient and flexible way to use spatial information. One of the key software components of an SDI is the catalogue service which is needed to discover, query and manage the metadata. Catalogue services in an SDI are typically based on the Open Geospatial Consortium (OGC) Catalogue Service for the Web (CSW) standard which defines common interfaces for accessing the metadata information. A search engine is a software system capable of supporting fast and reliable search, which may use 'any means necessary' to get users to the resources they need quickly and efficiently. These techniques may include full text search, natural language processing, weighted results, fuzzy tolerance results, faceting, hit highlighting, recommendations and many others. In this paper we present an example of a search engine being added to an SDI to improve search against large collections of geospatial datasets. The Centre for Geographic Analysis (CGA) at Harvard University re-engineered the search component of its public domain SDI (Harvard WorldMap) which is based on the GeoNode platform. A search engine was added to the SDI stack to enhance the CSW catalogue discovery abilities. It is now possible to discover spatial datasets from metadata by using the standard search operations of the catalogue and to take advantage of the new abilities of the search engine, to return relevant and reliable content to SDI users.

Keywords:

Data discovery

Catalogue Service for the Web

Metadata

WorldMap

Geoportal

Search engine

Spatial Data Infrastructure

pycsw

Solr

GeoNode

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