# A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs
## 5 Case Studies
### Path Recommendation  
In this case, our goal is to establish a series of routes that connect all pre-registered tourist attractions, with a specific emphasis on minimizing the overall cost rather than prioritizing the orientation of the routes. Initially, the data for analysis are sourced from OSM [65], specifically from a quadrant (Figure 11) within Guadalajara, Jalisco, Mexico. We follow the KDG methodology. Specifically, we use tasks 1.5, 2.1, 2.2, 2.3, 2.4, 3.1, 3.5, 4.1, 4.2, 4.4, 5.6, 6.1, 6.2, and 6.3. According to the methodology, we should check all tasks of stage 1. However, as in case 1, there are no defined business goals, information requirements, metrics, or project plans. Although tasks 1.1 to 1.4 are missing, the methodology indicates that we must apply at least task 1.5, in which we must define at least one question for decision making that guides the analysis to meet the initial request. In this scenario, we formulate the following decision-making question: Will at least one route allow us to visit all the tourist attractions? (task 1.5). Following the methodology, we advance to stage 2, where completing all tasks is mandatory. The first task (2.1) entails conducting an exploratory information analysis. The data for analysis are sourced from OSM [65], specifically from a quadrant (Figure 11) within Guadalajara, Jalisco, Mexico. Our analysis centers around the georeferenced information of a quadrant in Guadalajara, Jalisco, Mexico. Specifically, we aim to identify the paths linking the museums within this quadrant. We utilize OpenStreetMaps (OSM) to obtain the necessary data, which provides valuable geospatial information (tasks 2.1). Progressing to task 2.2, our graph model consists of two types of nodes: user and point. Since OSM is a community-driven platform, it tracks the users who edit each point or set of points on the  
Figure 10: The dashboard provides the following information: The top 5 countries with the most airports. The top 5 immediate connections, exclusively within the United States. The top 5 critical airports for ensuring continuity in the USA. The top 5 airports recommended for layovers in the USA. map. Additionally, the points on the map are identified by a unique identifier, latitude, and longitude coordinates. Some points may also have additional attributes, such as the brand name for businesses such as Oxxo or 7-Eleven, contact telephone numbers, street names, detailed descriptions, or indicators of being a tourist attraction. Although points have no inherent connection, we will establish relationships and incorporate distance information. The graph model is illustrated in Figure 12. Continue with task 2.3. We chose NEO4] as the data storage solution because it is a graph-based database. Subsequently, we extract the latitude and longitude coordinates of the sites classified by OSM as tourist attractions. Furthermore, we transform the OSM information into a graph-based database. Firstly, we export the data from the selected quadrant in Guadalajara as an XML file. Secondly, we develop a script utilizing the cypher language to convert each point into a node, extract their attributes, and insert the information into NEO4]. Each node includes essential details such as a unique identifier, latitude, and longitude. Thirdly, we calculate the geodesic  
Figure 11: The provided image depicts the quadrant associated with Guadalajara, Jalisco, Mexico, encompassing the georeferenced information from OpenStreetMaps. Figure 12: The general attractions graph model consists of a homogeneous and directed graph with attributes. It includes two types of nodes and two types of relationships. distance between every pair of nodes and establish a relationship called \"distance\" with the corresponding attribute \"geodesicDist\", containing the calculated value. These data are then incorporated into the graph database (task 2.4). Having completed all tasks in stage 2, we verified the sufficiency of the information and progressed to stage 3.