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example, if it is known that a feature has been updated recently at a source,
the feature at this source might be more likely to represent the correct location
information about the real-world entity. As with any other data integration
approach, the quality of the data plays a crucial role in resolving individual
spatial and nonspatial data conflicts. 54
Once different features have been matched to the same real-world entity,
the next step is to resolve conflicts that might exist among the descriptive
attributes. As these are ordinary attributes such as in relational databases,
respective approaches can be used. In the context of geospatial data such
attributes are typically based on metadata standards and application schemas
described in Section 10.2.3, which are likely to produce a more coherent data
description in terms of semantics.
10.3 Service-Based Data and Application Integration
In the following, we present emerging standards, techniques, and architectures
that enable interoperability among distributed and heterogeneous geospatial
data sources. In Section 10.3.1, we outline the relationships between interoper-
ability and data integration aspects. An overall framework for data integration
employed by the techniques presented in this chapter is the service-oriented
architecture, which is described in Section 10.3.2. In Sections 10.3.3 and 10.3.4,
we give an overview of service registry and geospatial Web services, respec-
tively. We place a particular focus on services that deal with real-time sensor
data, described in Section 10.3.5. We conclude the section with a brief overview
of a practically relevant alternative to geospatial Web services.
10.3.1 Approaching Integration through Interoperability
Interoperability among heterogeneous and distributed data sources is a fun-
damental requirement not only in the context of scientific data management,
but in any type of distributed computing infrastructure. Interoperability is
generally defined as “the ability of two or more systems or components to
exchange information and to use the information that has been exchanged.” 55
Interoperability can be achieved at different levels of network protocols and
data exchange formats. In several scientific application domains, interoper-
ability among data repositories and applications has become a main driver to
facilitate scientific data management and exploration on a large scale. Grid
computing infrastructures have significantly contributed to this development 56
and are widely employed in science domains, such as in Earth observation, 57
climate modeling, 58 and physics, 59 to name only a few. A more recent trend in
these science initiatives is to increase interoperability aspects through service-
oriented science . 60 A well-known early example that realizes such an approach
is the WorldWide Telescope. 61
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