Global Positioning System Reference
In-Depth Information
are information infrastructures to enable the sharing and (vertical and
horizontal) integration of resources needed by different stakeholders (e.g.,
scientists, policy makers, modelers, etc.) throughout the lifecycle of a project
(e.g., IM project).
Yet, open issues require further research such as to provide customized
VREs, i.e., the ability to incorporate the specifi c characteristics of each
discipline, and the exploitation of cloud computing paradigm. For the
latter, recent works are making good progress on effective geoprocessing
computation by exploring the interplay between geoprocessing services
and cloud computing platforms (Yang et al. 2011; Wen et al. 2013; Yue et al.
2013), as well as novel strategies for standardized reusable geoprocessing
packages that can be moved closer to data sources to alleviate exchanging
large amounts of data sets (Müller et al. In press). All in all, these works
pursue to bring pieces from distinct research infrastructures together.
Not One but Multiple Reproducible Resources
The Model Web highlights models which may be executed again and
again under similar conditions and settings. For doing so, it is necessary to
record in suffi cient detail the contextual information, also known as data
provenance, not only for a given model but also for any related resource
so that model can be fully reproduced later on. So, is Model Web through
scientifi c workfl ows concepts and web service technologies prepared to
capture model reproducibility on the Web?
The notion of
aggregation
is widely used in some scientifi c workfl ow
systems to bring together related resources into a logical unit. Aggregation
by defi nition implies reusability, i.e., an aggregation simply reuses the
resources it contains. For example, a geo-enabled scientifi c workfl ow
can aggregate varied types of resources such as workfl ow descriptions,
log records, inputs datasets and related publications and presentations
reporting on the execution results of that workfl ow.
Complementary to aggregation, data provenance allows users to
document each contained resource within an aggregation so as to determine
the usability, reliability and even uncertainty of such a aggregation as a whole
(Di et al. 2013; Rotmans and van Asselt 2001; Bastin et al. 2013). Uncertainty
management is a must in IM because contained models may potentially
utilize data sets from uncontrolled and unverifi ed sources. Recent works
have experimented data provenance techniques applied to geoprocessing
web services and compositions of web services (Yue et al. 2010; Yue et al.
2011; Jones et al. 2012), which pave the way towards the applicability of data
and model provenance to the Model Web vision. Nevertheless, the pending
question is how to map and implement provenance and aggregation in
conjunction with the concepts of reliability and uncertainty into Geo-enabled
Model Web vision.
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