4
Alberto Belussi, Barbara Catania, Eliseo Clementini, and Elena Ferrari
spatial data at the required resolution level on the fly. This could be very expensive in terms of computation time and thus “ad hoc” techniques have to be studied in order to improve the performance of such resolution level transformations.
Moreover, each type of spatial data requires specific approaches for dealing with
multiresolution representations and supporting resolution level transformation.
• Progressive Data Transmission and Visualization. Another problem that the
publication of spatial data on the Web has to face is the band availability for
data transmission. Spatial data set size is often measured in gigabytes or terabytes; thus the transmission of the whole data set at a finer resolution level
could take a very long time. Progressive data transmission can therefore be very
useful for spatial data; this technique is based on the following strategy: first, the
most relevant spatial details are sent at a coarse resolution level, then the user
can perform some preliminary operations and decide whether it is convenient
to wait for the detailed representation at a finer resolution level or to interrupt
the downloading. This allows one to save both time and disk space. A similar approach can be adopted for supporting the visualization of spatial data at
different resolution levels. In both cases, the key issue is the definition of data
structures and algorithms for increasing the performance of the resolution level
transformations.
• Semistructured Data Representation. The Web has also promoted a new approach for representing information that derives from the use of hyper texts
for presenting (using HTML) and for representing data (using XML). This
idea has produced the development of a new research area in the context of
databases and information systems, called “semistructured data management.”
As a consequence, new formal data models able to describe semistructured data
without depending on a specific tag language have been defined. The integration of spatial data in a semistructured data model is an interesting issue that has
been addressed in particular by the open geospatial consortium (OGC) through
the definition of the geography markup language (GML). However, new effort
is needed to integrate spatial and temporal properties in abstract semistructured
data models.
Part I consists of four chapters presenting complementary issues in the context
of spatial data modeling.
Chapter 2 presents a semistructured data model where spatial data are integrated
in the multimedia temporal graphical model (MTGM). This is an example of an
abstract data model for semistructured data with the ability to represent temporal
and spatial properties of objects. Moreover, a possible mapping to XML is proposed
that adopts the XML elements proposed by GML for spatial and temporal properties.
In Chap. 3, issues related to multiresolution representations for very large digital terrain data sets are discussed. In particular, the authors describe how to deal
with very large terrain data sets through out-of-core techniques that explicitly manage I/O operations between levels of memory. After a brief discussion about digital
terrain models, focusing on TINs, the authors review out-of-core techniques for
Alberto Belussi, Barbara Catania, Eliseo Clementini, and Elena Ferrari
spatial data at the required resolution level on the fly. This could be very expensive in terms of computation time and thus “ad hoc” techniques have to be studied in order to improve the performance of such resolution level transformations.
Moreover, each type of spatial data requires specific approaches for dealing with
multiresolution representations and supporting resolution level transformation.
• Progressive Data Transmission and Visualization. Another problem that the
publication of spatial data on the Web has to face is the band availability for
data transmission. Spatial data set size is often measured in gigabytes or terabytes; thus the transmission of the whole data set at a finer resolution level
could take a very long time. Progressive data transmission can therefore be very
useful for spatial data; this technique is based on the following strategy: first, the
most relevant spatial details are sent at a coarse resolution level, then the user
can perform some preliminary operations and decide whether it is convenient
to wait for the detailed representation at a finer resolution level or to interrupt
the downloading. This allows one to save both time and disk space. A similar approach can be adopted for supporting the visualization of spatial data at
different resolution levels. In both cases, the key issue is the definition of data
structures and algorithms for increasing the performance of the resolution level
transformations.
• Semistructured Data Representation. The Web has also promoted a new approach for representing information that derives from the use of hyper texts
for presenting (using HTML) and for representing data (using XML). This
idea has produced the development of a new research area in the context of
databases and information systems, called “semistructured data management.”
As a consequence, new formal data models able to describe semistructured data
without depending on a specific tag language have been defined. The integration of spatial data in a semistructured data model is an interesting issue that has
been addressed in particular by the open geospatial consortium (OGC) through
the definition of the geography markup language (GML). However, new effort
is needed to integrate spatial and temporal properties in abstract semistructured
data models.
Part I consists of four chapters presenting complementary issues in the context
of spatial data modeling.
Chapter 2 presents a semistructured data model where spatial data are integrated
in the multimedia temporal graphical model (MTGM). This is an example of an
abstract data model for semistructured data with the ability to represent temporal
and spatial properties of objects. Moreover, a possible mapping to XML is proposed
that adopts the XML elements proposed by GML for spatial and temporal properties.
In Chap. 3, issues related to multiresolution representations for very large digital terrain data sets are discussed. In particular, the authors describe how to deal
with very large terrain data sets through out-of-core techniques that explicitly manage I/O operations between levels of memory. After a brief discussion about digital
terrain models, focusing on TINs, the authors review out-of-core techniques for