## 4 Use Case
### Discussion  
Linked Data facilitate the reuse and enrichment of data by using external sources, hence enabling the extension of the data models. As a result, data integration and exploration through complex relationships become more efficient and easier. Federated queries allow the execution of queries distributed over different SPARQL endpoints. In addition, these queries allow the inclusion of new non-existent attributes in the original data source. In our experiments, we have enriched the original data with external information from different repositories, such as Wikidata and GeoNames. The enrichment allows for filtering the results according to the new attributes of a specific zone (i.e., the population or the per capita income). Based on this new information, we will be able to extract new indicators, as well as new indicator correlations that will improve the decision-making process. For instance, a new correlation between the water supplied and the population, or water leaks of a specific zone could be discovered. In this context, given that the information provided by the water company is accurate in terms of completeness, consistency and quality, the ontology model could help in identifying correlations between internal indicators and external information. For example, an increase of the temperature may cause an increase in water demand while an increase of per capita incomes in a specific zone may imply a higher demand of water. In the use case, water usage versus population change has been presented. Similarly, additional examples such as water usage versus temperature or water usage versus per capita incomes could be explored to find out hidden correlations. In this regard, the decision-making process can be enhanced by means of additional metrics, facts, or figures gathered from external datasets providing valuable insights. Furthermore, this information enrichment takes into account the context for making decisions. That is, for each zone, the manager could retrieve related external information in order to discover insights for each specific area. For example, the water consumption of _Zone 2.1_ could be influenced by temperature because it is extremely hot; otherwise, in _Zone 8.2_, it could happen that temperature did not have a direct impact on water consumption since it is not an extreme temperature. In conclusion, the granularity allows us to customize each subzone with the relevant factors that influence water consumption. With respect to data access and integration, many LOD repositories have recently appeared in a broad sense. However, there is still limited capacity regarding specific sectors when reusing the datasets due to different reasons such as the use of traditional formats (e.g., PDF) that are not accessible in a easy way. In this context, the gradually increasing use of LOD may help with the integration of different datasets. In addition, it is important to notice that, while the use of public SPARQL endpoints enable the integration of the internal information facilitated by the company with information provided by external repositories, its use requires IT skills. Figure 6: Supplied water and population of the zone 4.1. from 2008 to 2014. It is worth noting that a possible comparison between the data extracted using the ontology-based model versus model without ontology, lies in the fact that the former provides a common framework in which the data are integrated and able to provide machine-readable queries (that can be easily adapted for each case), whereas in the latter case it always requires the reimplementation ad-hoc adaptations, since there is no a standard and fast methodology for data access and integration. Therefore, comparisons in this sense should focus on the time spent for data integration, which thanks to our ontology-based model is possible to avoid the manual reimplementation of these processes, just by adapting new SPARQL queries. This is advantageous from the point of view of the acquisition and availability of quality data.