Apart from the written text, a database can be used to store the design decisions. The database was tested in a twofold execution. The traditional relational database was tested first and, secondly, the novel graph database. Both of them contain the same data and there are only slight differences in their structure. However, graph databases have never been used for the encoding of uncertainty in three-dimensional models and therefore constitute an experimental and novel approach.
One major advantage of relational databases over graph databases is the ability to store digital media content. Those could be, for example, photographs of comparison structures or scans from the literature. Another one is their prominence. They are widely known and used. Consequently, many software solutions with a graphical interface are available. The data itself is stored in big tables and can be linked over indices. The relationship itself plays only a minor role. In contrast, relationships are a central component of graph databases. Accordingly, they emerge between interpretation and source. Due to the large number, the graph database clearly benefits from this structure. Likewise, it provides a set of queries that are specifically designed to investigate highly connected data. Nevertheless, even without filtering the data, first impressions can be obtained. This can be among other things, a rough overview of the discussion or the direction of individual lines of argumentation. Lines of argumentation are indicated by the direction of the relationships between the nodes. This considerably simplifies the assignment of individual uncertainty values, since they are assigned to the relationship instead of the source.
120 One problem with the relational database and graph database is the assignment of actual uncertainty indices. In both cases they are highly subjective and might vary from case to case. A theoretical workaround for a graph database (fig. 64) might offer a solution to this difficulty. Essentially, it includes three components. Firstly, the impact of the source is determined by counting the incoming relationships towards a value and taking it as a multiplicative inverse. Secondly, the reliability is coded into the relationship as the
subjective opinion of the author. It expresses one’s confidence about how reliable the
argumentation is. Importantly, it can vary for one source. This is because it is not always the case that all the argumentation of one source is reliable. Finally, the authority
determines how many values one source can prove. If importance, reliability and authority are combined, a value is received that is anti-proportional to the object’s
uncertainty. The higher the value, the lower the uncertainty. However, this statement represents a concept that still has to be balanced and normalized in order to use it correctly. To achieve the same workaround in a relational database, much more complex algorithms are necessary. Graph databases are naturally capable of this.
Figure 64: Concept drawing for an approach to determine fuzzy-based uncertainty in a graph database. Basically, incoming and outcoming relationships are evaluated and brought together. This idea will work effectively only within one classification and has to be updated frequently (Brunke 2017).
Further advantages of the graph database are the use of multiple labels for the same node, providing a fast and effective way to query and order large amounts of data. Likewise, only minor changes are necessary to exclude or include specific parts. However, a major weakness of graph databases is the lack of a graphical user interface and the resulting size of the cypher queries. The use of keys (fig. 65) is common for the relational and graph database. They are necessary to identify individual segments and trace back the path of argumentation. Moreover, they also serve as an interface for the cad software by using them as names for the objects. Since both databases provide a python api, it is possible to easily query datasets from third-party applications in the form of scripts. Graph databases also allow returns in form of tables and lists.
121 Figure 65: Structure of a key. Basically, it follows the segmentation from chapter 3.3 Classification of the architecture and 4.1 Segmenting and organizing the data, whereby the individual elements can be changed as desired. A dataset owning a key means that it belongs to the segment encoded into the key (Brunke 2017).
Generally speaking, the relational database seems to be best suited to fuzzy-based uncertainty since it includes only a list of numerical values, while class-based uncertainty and/or a combination of both can draw real strength from the relationship-based nature of graph databases. However, the graph database, which is rarely used, especially offers huge potential. It seems it would be promising to examine this form of database in more detail. It might be even possible to discover new ways of documenting uncertainty, but also to analyze it. Likewise, it seems appropriate to develop more user-friendly interfaces with regard to archaeology. One of them could be Blender, which connects the 3D objects directly with information in the database over a Python script. Furthermore, Blender already offers some valuable tools for organization and analysis. More will be explained about it in the following sections.