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indeed the software components and techniques are applicable to the vast
majority of public and private cloud infrastructures.
Contribution
By assembling software components on public cloud infrastructures,
our approach is arguably extensible and open. Furthermore, as part of
the recent trends of increasing reproducibility of software engineer-
ing contributions, we are publishing the entire software environments
together with this chapter such that any cloud developer can use them.
7. 2 Bac kg r ou nd
Incorporating geospatial and descriptive data, GISs are a holistic integration
of hardware, software, and standardized formats for capturing/encoding,
managing, analyzing, and displaying all forms of geographically referenced
data [8]. GISs have long been used beyond the boundaries of geography, and
they are typically an aggregation of nonhomogeneous architectural plat-
forms, applications, and processing needs due to a heterogeneous universe
of users in science, business, and society in general [16].
Formerly, organizations had to buy dedicated GIS software packages to
use and manipulate data over their network. Currently, web-based GIS soft-
ware packages are readily available, and many organizations use web-based
GISs to increase their availability of information for public and internal use.
However, one of the biggest problems with large GISs is that all data are not
necessarily available from the start, and systems are commonly rolled out
following geographic area patterns rather than system usage or resources.
Moreover, large GIS projects usually start from a small amount of data but
expand rapidly as data increase, requiring expansion of installed resources.
Ergo, system resources are not consumed in predictable patterns as differ-
ent users may follow seasonal or incident-driven usage patterns. In time, as
more data are collected, systems cover additional geographical areas, and
this leads to the need to increase other resources required by the system.
A canonical example is Google Maps. It can be seen that the street view is
not available for every place in the world, but these views are growing with
time as Google collects data.
The operations performed on geospatial data within a database require
significant computational resources for processing, typically surpassing the
standard departmental infrastructures of small- and medium-size enter-
prises. For example, the selection of locations (points) that reside inside a
given region (polygon) is particularly computationally demanding as the
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