Global Positioning System Reference
In-Depth Information
Goodchild et al. (1993) topic on environmental modeling and Geographic
Information Systems (GIS) and Abel et al. (1997) paper on integrating
modeling for environmental management information systems, to the recent
Environmental Modelling and Software's thematic issue on “the Future of
Integrated Modelling Science and Technology” (Laniak et al. 2013a), there
have been proposed lots of methodologies, strategies and approaches
to deal with IM projects. In the 1990s, fi rst approaches were focused on
the integration of environmental models into desktop-based GIS tools.
Existing GIS packages were containers to embed environmental models
on such desktop tools. In the next decade, with the advent of the Internet
and Web technologies, modeling strategies shifted from ad hoc, desktop-
based solutions to distributed computing and web services technologies.
Multiple middleware frameworks emerged to cope particularly with IM but
still tailored to specifi c disciplines and fi elds (e.g., hydrology and ecology).
Last years, though, have been particularly active with the emergence of
new computing paradigms (e.g., cloud computing) and novel approaches
such as Model Web and resource-oriented architectures which altogether
promise platforms and tools to readily enable cross-domain IM.
In the following we present a state-of-the-art on technologies, systems
and frameworks for supporting IM, i.e., the integration of various models.
For doing so, we particularly focus on the concepts of workfl ow and services
which drive scientifi c and business workfl ow systems and geoprocessing
web services respectively.
Scientifi c Workfl ow Systems
Deelman et al. (2009) introduced the concept of workfl ow as the “activity of
defi ning the sequence of tasks needed to manage a business or computational
science or engineering process”. A more specifi c defi nition would be a set
of analytical tasks that may involve different processing tasks such as data
access and querying, data analysis and processing, and data visualization.
Workfl ow designers often specify a control fl ow (e.g., sequence, forks,
switch, joins, etc.) and data fl ow (how outputs of a preceding task connect
to the inputs of subsequent task) in order to structure the order of the
required steps or activities.
In the scientific context, most tasks consist of the acquisition,
manipulation, documentation, and processing of large amounts of scientifi c
data, as well as the execution of computationally intensive analysis and
simulations (Ludäscher and Goble 2005). Scientifi c workfl ows may be
seen as a description of the combination of the previous scientifi c tasks to
address data-centric applications. Such descriptions are then managed by
scientifi c workfl ow systems, which are able to interpret and execute every
single task specifi ed within a scientifi c workfl ow.
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