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Title
New perspectives to save cultural heritage
Author
Altan, M. Orhan

CIPA 2003 XIX th International Symposium, 30 September - 04 October, 2003, Antalya, Turkey
714
recording and analysis of the geoglyphs, which are distributed
over a wide and hardly accessible area. In close cooperation
between archaeologists and geodesists at ETH Zurich, the
Nasca lines at Palpa were mapped and modeled in 3D on the
base of a series of aerial images especially acquired for this
purpose (Griin, Lambers, 2003; Sauerbier, Lambers, 2003). This
is the first fruitful application of photogrammetry to Nasca
archaeology and a significant step towards the documentation
and preservation of the Nasca lines, which nowadays are
heavily affected by erosion and modem land use (Lumbreras,
2000). Furthermore, we are currently designing and
implementing a geographical information system to analyze and
interpret the role of the geoglyphs in the Nasca cultural
landscape at Palpa (Griin et al., 2003; Sauerbier, Lambers, in
press). As an important step on the way to an explanatory GIS
(Wheatley, Gillings, 2002: Fig. 12.1), in this paper we describe
the central part of our data model that shows how we organize
the spatial and attribute data on the Palpa geoglyphs in order to
enable a meaningful archaeological analysis.
Fig. 2: Geoglyphs etched into the desert surface on a slope near
Palpa (lines, areas, figures)
2. THE DATA MODEL
The design of a conceptual data model is the first step during
the development of a GIS. A conceptual data model is system
independent and formally describes the real world phenomena
which are to be modeled in the information system following a
semantical model (e.g. relational, normal form, entity
relationship model, object oriented) (Heuer, Saake, 2000). After
a conceptual data model has been designed, it can be adapted to
a specific database management system (DBMS) and
implemented by developing a logical data model. Then, based
on the logical data model, the next step is data definition. For
this purpose, the logical schema is transformed to an actual
database schema using the specific data definition language
(DDL) and the data manipulation language (DML) of the
chosen DBMS. The result of data definition is a description of
the conceptual schema and the external views according to the
Three-Tier Schema Architecture (Tsichritzis, Klug, 1978).
Finally, the physical data model defines the internal level. It is
an enhancement of the data definition concerning the
improvement of access and efficiency, e.g. using additional
search indices for those attributes that are typical selection
criteria in queries.
2.1 Conceptual data modeling with UML
Here we focus on the design of the conceptual data model, the
first step in the modeling process. As mentioned above, in
information sciences a wide range of modeling languages exist.
For the development of a data model for the Nasca-Palpa
project we chose UML™, an object oriented approach for
conceptual modeling. UML (Unified Modeling Language;
Object Management Group, 2003) is an enhancement of the
widely used Entity Relationship Model (ER Model). It is
currently one of the most common languages for conceptual
data modeling. In our case, we chose it for two main reasons:
1. The object-oriented approach is especially well suited to
structure the attributes of archaeological objects according
to the requirements of archaeological typology. The
elaboration of different typologies will be the first step in
data analysis once the GIS database is established.
2. At ETH Zurich, Rational Rose 2002 is available via a
campus license. This software package allows graphical
modeling and the export of the conceptual data model to
different DDLs of common DBMS (in our case Oracle 9i)
for a largely automated derivation of the data definition
process in terms of SQL-DDL scripts. An interactive
implementation of the table structure inside the DBMS is
possible.
In the following, the class diagram showing the current status of
the most important subset of the conceptual data model, the
geoglyphs, will be explained in detail. The present conceptual
data model is based on a model developed in an earlier stage of
the project (Visnovcova, 2000), which was enhanced to a more
detailed level and adapted to new requirements. The objective
of the data model is to store the existing geometrical and
archaeological data in a structure that allows a combined as
well as an independent analysis of both kinds of data
(Sauerbier, Lambers, in press). This requires an appropriate
design of classes, relationships and integrity constraints in terms
of the planned analyses, queries and data manipulations. Since
the geometry will be stored in the pre-defined Oracle spatial
data objects (SDO) structure (Sharma, 2002), this primarily
concerns the archaeological data and its relations to the
geometry.
2.2 The class diagram
The central part of the data model represents the geoglyphs
themselves. The technical and archaeological characteristics of
these distinctive ground drawings have been described in detail
elsewhere (Reindel et al. 2003; cp. Fig. 2). Most information
available on the Palpa geoglyphs was obtained during fieldwork
in Palpa. The attributes described and stored in a MS Access
database include location, shape, construction technique,
context, stratigraphy, associated finds and structures, etc. Some
of these attributes assume only a limited set of values and can
therefore be used for an initial sorting of the more than 1500
geoglyphs following formal criteria (Adams, Adams, 1991).
These attributes include shape, construction technique, and size.
Due to their importance for analysis, they should be modeled in
an especially efficient and easily available way following the
object-oriented approach. Once an initial sorting is achieved,
contextual attributes can then be considered in order to establish
meaningful (e.g., functional or chronological) typologies.
In the class diagram (Fig. 3), the geoglyphs are represented by
the central, first level class “A_Geoglyphs” ("A_..." denoting an
archaeological object), under which the attributes shared by all
geoglyphs are listed. These attributes are inherited by the
instances of all subclasses, just as certain methods that also
apply for all geoglyphs. The subclasses represent values of the
aforementioned higher weighted attributes, e.g. shape. The