Express and detect design decisions

In order to find the right form of database, it is important to understand the existing data. That includes knowing its type and origin. Likewise, it is also important to determine what kind of analysis might be done with it. In this case, the main concern is uncertainty. However, all interpretations and assumptions are based upon data. In an ideal case, each of the processes involved is somehow documented. However, how are uncertainty and especially the design decisions stored to provide a transparent result? Firstly, all sources and available data are classified (= class-based uncertainty)14_{. Each classification can} represent an own level of certainty (Wittur 2013, 38). Usually a gradient from certain to uncertain is used. In a later processing step, each value of the gradient can be assigned to a specific visualization representation, such as a color code. Apollonio and Giovannini use this method. However, the background data is more important. The sources are ordered in terms of reliability, where the most reliable are actual measurements and least reliable are missing data. The space between is filled with a smooth transition. In this case a certainty scale is applied in relation to the geometry of a building structure, with classifications of possible sources (Apollonio and Giovannini 2015, 8).

By contrast, Strothotte does not use an uncertainty scale at all. He describes it as “design

decisions […] Type of reason”. Those are classified in “excavation […] physical constraints

[…] period features […] analogies […] deductions”. However, in the end they still represent a gradient from certain to uncertain (Strothotte et al. 1999, 3f). Dell’Unto’s approach is to

encode the uncertainty of the reconstruction in terms of “objectivity […] testimony […] deduction […] comparisons […] analogies […] deductions”, which likewise means the same as the two approaches mentioned above (Dell’Unto et al. 2013, 624f). In contrast to the former methods, he gives the analogies a much higher degree of reliability than the other authors. Furthermore, he directly represents the uncertainty rather than a degree of

14 _{For the sake of clarity, this method will hereafter be referred to as “class-based uncertainty”. The}

43 uncertainty. Each class is assigned to its own color, which is directly applied on top of the mesh surface (fig. 11) (Dell’Unto etal. 2013, 626). In Apollonio’s publication, he follows a

similar approach. Classes are described by the sources and each class is allocated a color.

The term “uncertainty” is disregarded completely, since everyone can decide for him- or herself (Apollonio 2016, 187). In general, it should always be made clear that, at one point, all discussions, arguments and assumptions are described in a structured way (Wittur 2013, 38).

Figure 11: Example of class-based uncertainty. The reconstruction represents the atrium of a building in

Pompeii. A color code is used as coding for the classes (Dell’Unto et al. 2013, 626).

Thirdly, instead of using words as synonyms for the uncertainty, direct numerical values can be used (= fuzzy-based uncertainty)15_{, as in Hermon and Nikodem’s approach (fig. 12).} With the help of fuzzy logic they calculate explicit values out of a reliability and importance index. This value is treated as an uncertainty index and assigned to building components (Hermon and Nikodem 2008, 4f). However, their approach lacks some points of explanation or transparency in the discussion, since the original values represent only the

authors’ opinions about their confidence. Kensek et al. seem to use a similar approach,

since their visualizations allow for conclusions about absolute values in their automatic reports (Kensek et al. 2004, 178ff).

15 _{For the sake of clarity, this method will hereafter be referred to as “fuzzy-based uncertainty”. The}

reason for this is that the numerical values are based upon fuzzy logic. Sources are also involved here. However, these are enriched with numeric values.

44 Figure 12: Example of fuzzy-based uncertainty. The reconstruction represents a Roman house in Pompeii. A color code is used to encode the numerical values (after Hermon and Nikodem 2008, 5).

Finally, instead of several classes, Boolean operators or crisp sets can be used. Bakker et al. differentiate only between the actual remains of the building and his interpretations (Bakker et al. 2003, 4). Thus, he has only two states, namely certain (photorealistic) or not certain (schematic) (Bakker et al. 2003, 4). Reimersdahl et al. use a kind of mixture. Their datasets contain more states than certain and uncertain. However, the visualization shows only two classes, whereby certain areas are encoded by photographs (Reimersdahl et al. 2007, 4). However, this case is an exception. Likewise, Patay-Horváth also follows the principle of crisp sets when visualizing the statues of the Zeus temple (Patay-Horváth 2014, 18ff). A Boolean visualization can be found in many papers (fig. 13).

45 Figure 13: Example of a Boolean representation. The rendering shows the walls and gates of the Athenian Acropolis. The less saturated textures are uncertain, while the more saturated ones are certain (Eiteljorg 2000).

However, uncertainty can also be completely neglected in visualizations and documentation. Case studies related to this are not further discussed in this chapter, since they do not improve the result. Nevertheless, Sifniotis et al. investigated the extent to which sources can influence the uncertainty of a model. He asks other scholars to order

“Features […] Artefacts […] Biofacts […] Textual evidence […] Absolute comparison […] Contextual comparison […] Topography […] Peer review” according to their reliability as sources for reconstructions. His results are indicative of the fact that most scientists agree with the lowest and highest term. However, the mid-levels might vary (Sifniotis et al. 2007, 7). Therefore, most reliable and powerful seems to be the actual feature or artefacts.

In general, however, three concepts of uncertainty can be distinguished. Firstly, there is source classification. Different sources are grouped together and classified according to their relative uncertainty. In the course of this work, this approach is described as “class- based uncertainty”. Secondly, there is the numerical allocation of uncertainty values to an

object. Hereby, the scholar assigns his confidence in the form of a number to a specific object. This is correspondingly best described as fuzzy-based uncertainty. Finally, there is the use of Boolean operators, which allow for only two states, namely certain or non- certain, or reality-based and evidence-based. As can easily be seen in the illustrations, certain data types fit best with certain databases and also a certain type of visualization. The difference can be seen in the choice of colors. The classifications are represented by

46 several contrasting colors, while the numerical values use a gradient of one color. The Boolean value must show only a high contrast. Like in above’s paragraph indicated many visualizations consist of static images. Not always this form of medium is optimal for archaeology. Other formats might be animation or interactive models and are described following.