# Multi-Purpose Ontology-Based Visualization of Spatio-Temporal Data: A Case Study on Silk Heritage
## 2 Related Work  
### Visualization of Spatio-Temporal Data  
Information visualization (often shorted as InfoVis [9]) is a recent but important research area. Due to the increasing importance and use of data analytics in almost every scientific field, information visualization is necessary in a variety of applications. In a broad sense, InfoVis can be divided in five different steps, which form the _visualization pipeline_[10]:  
_Step 1. Data collection and transformation_. First, we need to collect data from their sources. Data is sometimes unstructured. Therefore, it is necessary to transform it in order to structure it appropriately. _Step 2. Filtering_. Then, it is usually necessary to filter the data, in order to remove noise and focus only on what is meaningful for our visualization purposes. This step, which reduces significantly the amount of information that we need to handle, can only be done if data are previously sufficiently structured. _Step 3. Mapping_. In this step, we need to map the filtered data into geometric elements (points, lines, circles, squares, bars etc.) that can be graphically represented. _Step 4. Rendering_. A rendering module is necessary, in order to arrange all these geometric primitives and create a visual representation. _Step 5. User Interaction_. The last module is a user interface, by which users can modify the visualization method/parameters and/or explore different datasets. In many occasions data are multifaceted, since they can be multidimensional, multimodal (from different sources), multivariate (with different attributes) or multimodel (coming from different models) [9]. This heterogeneity is often a problem for InfoVis and most visualization approaches rarely address all these facets. The case of spatio-temporal visualization is a particular case of multifaceted data visualization where the dimensions of time and space are used to plot how another (or others) variable(s) vary in space and time. Although much research has been carried out in this area, these data are difficult to visualize when several dimensions, in addition to spacetime, are considered simultaneously [11]. This often leads to ad hoc strategies for the visualization of spatio-temporal data with multiple dimensions. In order to formalize this, a series of classifications and strategies on spatio-temporal data have been proposed. These strategies and structuring levels define what can be queried for each level and what can be obtained from the system by performing queries. For instance, in [12], three main parts are identified in spatio-temporal data: where (location), when (time) and what (objects). The last item represents the independent variable(s) that need(s) to be plotted with respect to spacetime. This simple idea is also used in [13], offering a classification of visualization methods with respect to the typology of tasks. Several InfoVis tools use this where + when + what approach. The main strategy is to apply this idea to a bi-dimensional or three-dimensional region. Several techniques, such as color maps, color clustering, heat maps, elevation maps, bubbles or labels are used to represent information [14; 15; 16; 17; 18; 19]. Most of the previously shown strategies take place in the rendering step of the InfoVis pipeline. Of course, the rendering step is not the end of the visualization process. User interaction is also necessary for users to control and filter the information they want to see. Time information is commonly addressed in this manner, since a map often represents the situation in a particular time. One of the most used interactive strategies is the use of a timeline, which allows controlling the time frame in which the spatial visualization occurs [20; 21; 22; 23; 24; 25; 26]. This strategy is frequently used in CH, since in this type of applications is often necessary to see different time periods, and switch between them interactively.