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RELATIONAL DATABASE MANAGEMENT SYSTEM
FOR THE EARLY METALLURGY OF COPPER AND BRONZE
IN TRANSYLVANIA, ROMANIA
Manuella Kadar1, Valentin Bucur 1, Emilian Ceuca1
“1 Decembrie 1918” University of Alba Iulia, Department of Mathematics- Computer Science
Nicolae Iorga street 11-13, 2500 Alba Iulia
[email protected], [email protected], [email protected]
Abstract. In order to approach the problems of bronze metallurgy in their full complexity, we have considered that the design of a
relational database management system is required.
Regarding the database design we have given a special attention to the problems concerning the relational model and the query
system of the database in order to obtain complex reports. This application is designed to be a fundamental tool for organizing and
developing a complex archaeological study. Thus, the database has a bibliographical collection, a collection of properties, a
geological distribution with image facilities, an archaeological repertoire, etc. The metal finds belonging to Neolithic, Eneolithic and
Early Bronze Age cultures of Transylvania have been classified, on the bases of metallographic analyses and chemical composition.
Typological classification of artefacts is correlated with the results obtained by analytical methods, in order to discuss the
provenience of raw materials, the metalworking techniques in this region, and their relationships to other early metallurgical centers
from Central and South-Eastern Europe. An interdisciplinary, methodological approach has been adopted, which uses the latest
analytical techniques, integrating various methodological and theoretical means of design, dissemination and validation.
Keywords; relational database, copper, bronze, metallurgy, Transylvania, chemical composition, metallographic analyses
INTRODUCTION
DESIGN AND MODELING
This application has been designed as a tool serving an
interdisciplinary project focused on the early metallurgy of
copper and bronze in Transylvania. Situated within the
Carpathian Arch, Transylvania is important for its natural
resources and strategic geographical position starting from
prehistoric times.
Design was based on the traditional theory consisting of three
steps: analysis, design and implementation. The conceptual
model, which is independent of the target technology has been
created in the first phase, then the logical data model and the
physical model are ready for implementation.
Starting from the relational model the physical implementation
had to fulfill some performance requirements imposed by index
configuration, data placement, storage allocation, space
optimization.
The theory of the independent metallurgical canters which were
developed in the Balkans versus the origins of metallurgy in the
Near East and its spread through intensive cultural exchanges
from Anatolia or from the Aegean area is the key issue discussed
in this project. Our analyses would like to complement the
analyses of metal artifacts from Serbia, Bulgaria, Greece and
Anatolia in order to be provide a basis for the interregional
comparisons.
Reconstruction of production and exchange systems in
Eneolithic and Bronze Age Europe and the identification of
geological sources for raw materials available in Eneolithic and
Bronze Age are the questions to be answered by this study.
Typological classifications of artefacts correlated with
classifications based on chemical composition and
microstructure analyses provide key information on the specific
features of metalworking techniques in this region and
comparison with other early metallurgical centres from Central
and South- Eastern Europe.
A data warehouse has been created by using the basic principles
of data modelling and database design, including new techniques
of dimensional data modelling (star schema) and normalized
dimension tables (snowflake schemas) have been introduced to
support design.
One of the most important problems for the implementation of
RDBMS (Relational Database Management System) is the
selection of indexes. An index that improves data retrieval
performance may degrade performance for all kinds of updates,
because the maintenance cost for the index has to be paid for
each update. Selecting the optimal indexes for a given set of
tables (an index configuration) is a laborious process that
requires trade-offs between the different kinds of database
operations (retrieval operations, update transactions, and
utilities). Given an index, one must determine its properties, such
as the column ordering for multi-column indexes, and whether or
not the index should be clustered, or partitioned ordered.
Space optimisation had to be taken into account when designing
relational databases. Allocating enough space to maintain the
clustering properties, without wasting too much space along
index selection and data placement provide the structural stability
of the relational model.
The relational model is presented in figure 1.
Figure 1. Relationships
Considering the advantages of the star schema for dimensional
modelling (e.g. easy to understand; hierarchies are easy to define;
one can reduce the number of physical table joins; low
maintenance; the meta data is very simple) the method has been
used in this application. Some goals we have kept in mind were:
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To maintain data at the conceptual level
To transform a valid, complete model to a
relational design
To design an optimal database
To reuse data definitions or relational designs.
As general design methodology an interdisciplinary approach has
been adopted that uses the latest analytical techniques and
integrates various methods of assessment such as artificial neural
networks in statistics. The database is designed as a support for
organizing and developing of this archaeometallurgical study.
Artefacts are considered in their complex archaeological and
social context in order to describe as accurately as possible the
technological level of the communities who have created those
artefacts.
Some of the features we have taken into consideration with regard
the design of the database were:
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Detailed, accurate, up to date documentation of the
metallic artefacts
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Easy access, processing and filtering of data, facilitating
research in the field of human culture and development
of technology
Objective storage of data, enabling all interested
researchers to get acquainted with it and with various
interpretations made by scholars
Interpretative observations resulted from the subjective
view of the individual researcher, such as cultural or
functional attributes, have been entered into special
fields labelled ”Notes” in order to provide maximum
level of objectivity.
Connections to other reference databases and
bibliography has been provided in order to offer all the
existing information on a specific issue.
DATABASE STRUCTURE
The database was created in Access 2000 which provides userfriendly programming, easy upgradability, and the likelihood of
continued upgrades in the future. The base is still in the
experimental phases and it has been tested on three counties of
Transylvania: Alba, Hunedoara and Cluj. The main access frame
is a clickable map representing all counties from Romania, being
active within the Carpathian Arch.A language option has been
implemented in order to make the information available to
foreign researchers as well (figure 2).
All tables are presented both in English and Romanian and
searching is available in both languages.
Figure 2. Main interface of the database
A central concern in creating the interface was to create a simple
clean set of forms which give access to a complex set of tables.
Data entry could have been achieved only through a “high level“
knowledge of the database system and a more in depth
knowledge of archaeological recording systems. This restricted
the casual user but meant that data entry became more of an
intelligent process required in creating archaeological archives.
The documentation system starts with a General Form
comprising all the scannings and measurable data with special
fields for personal observation and interpretation. General data
about place and type of storage, general description of the piece
can be seen in the main form.
Within the main form one can choose links to other sub-forms in
order to get a more comprehensive information. These sub-forms
are presenting the archaeological data, technological data,
metallographic data , chemical composition, micro-radiography
and micro-photography, general and specific bibliography
connected with each sub-form.
Archaeological data includes information on the excavation, site
description, location of finds, circumstances of the unearthing,
horizontal and vertical stratigraphy, cultural attribution and fields
for observations and bibliography.
Technological data refers to functional attributions, macrostructural examination, technological and ornamental description,
signs of wear, various possible treatments to which the objects
has been submitted after its recovery, state of preservation, etc.
This description include also dimensional characterization, type
of material and photograph of the object, drawings (figure 3).
Metallographic data presents the identification of the
microstructure and methods of investigation applied to each
object. Comparisons with previous analyses achieved by other
research teams are available. This sub-form is connected with a
collection of digital images representing different magnifications
of the microstructure.
Chemical composition includes various methods of
investigation, which have been applied to each object. Results are
available in tables and comparisons between methods applied
during the last centuries in order to investigate the composition of
early metallic objects are provided in the observations and notes
section (figure 4).
The search in the database is possible on two levels, through the
commands FIND and FILTER. FIND has been applied to the
graphical search connected to the map showing counties and
localities where artefacts have been discovered. In each chosen
locality from the map, a list of artefacts will appear on the right
side of the window, allowing the user to obtain more complete
information in a printable report. FILTER command is working
similarly but with the facility of filtering data using one or more
criteria. The criteria options for filtering that are included in the
main starting frame are: historical period, locality, object’s name,
object’s category, object’s type, type of material, type of
archaeological site.
Figure 3. Technical description of the artefact
Figure 4. Chemical composition sub-form
FUTURE DEVELOPMENT
Systematic construction of the data base in a powerful relational
structure seems to be the key issue for usability and
modifiability in the future. We are continuing to invest
considerable effort and student training in developing this
database and to extend it to other regions of Romania, as well.
Another direction of development will be the implementation of
a three-dimensional reconstruction of sites with all features
consisted in, by using data on the elevations. After the testing
and monitoring of this archaeometallurgical database will be
finalized, we intend to inter-link it with other independent bases
and archives in a graphic environment set up on the INTERNET
in order to permit access to various levels of research and to
increase the consulting and management possibility with regard
to the cultural heritage of Romania.