Database Reference
In-Depth Information
this chapter proposes to enrich the current temporal metadata with the possibility to indicate temporal
metadata related to both the observations, i.e., the geodata, the observed event, i.e., the objects in the
geodata, and the temporal resolution of observations, i.e., their timestamps. The chapter introduces
also a proposal to manage temporal series of geodata observed at different dates. Moreover, in order
to represent the uncertain and incomplete knowledge of the time information on the available geodata,
the chapter proposes a representation for imperfect temporal metadata within the fuzzy set framework.
Another issue that is faced in this chapter is the inadequacy of current discovery service query facilities:
in order to obtain a list of geodata results, corresponding values of metadata must exactly match the query
conditions. To allow more flexibility, the chapter proposes to adopt the framework of fuzzy databases to
allow expressing soft selection conditions, i.e., tolerant to under-satisfaction, so as to retrieve geodata
in decreasing order of relevance to the user needs. The chapter illustrates this proposal by an example.
INTRODUCTION
Infrastructures are complex systems in which a network of interconnected but autonomous components is
used for the exchange and mobility of goods, persons, information. Their successful exploitation requires
technologies, policies, investments in money and personnel, common standards and harmonized rules.
Typical examples of infrastructures which are critical for society are transportation and water supply. In
Information Technology, the term infrastructure could be related to communication channels through
which information can be located, exchanged, accessed, and possibly elaborated.
The importance of Spatial Data Infrastructures (SDIs) has been recognized since the United Nations
Conference on Environment and Development in Rio de Janeiro in 1992.
Geographic information is vital to making sound decisions at the local, regional, and global levels.
Crime management, business development, flood mitigation, environmental restoration, community
land use assessments and disaster recovery are just a few examples of areas in which decision-makers
can benefit from geographic information, together with the associated Spatial Data Infrastructure (SDI)
that support information discovery, access, and use of this information in the decision-making process.
In time, the role of discovery services of data with a geographic reference (geodata) has become a
main issue of governments and institutions, and central to many activities in our society. In order to take
political and socio-economics decisions, administrators must analyze data with geographic reference;
for example, the governments define funding strategies on the basis of CO
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pollution distribution. Even
in everyday life, people need considering data regarding the area in which they live, move, work and
act; for example, consider a family wishing to reach mountains for a skiing holiday, and looking for
meteorological data. In order to be useful, the data they are looking for should fit the area and period
of time of their interest; they should trust in the quality of the data; if possible, they should obtain what
they need with simple searching operations, and in a way that allows evaluating the fitness of the data
with respect to their needs and purposes.
In 2007, the INSPIRE Directive of the European Parliament and of the Council entered into force
(INSPIRE Directive, 2007) to trigger the creation of a European Spatial Data Infrastructure (ESDI)
that delivers to the users integrated spatial information services. These services should allow users to
discover and possibly access spatial or geographical information from a wide range of sources, from the
