# OntoTouTra: Tourist Traceability Ontology Based on Big Data Analytics
## 8 Discussion and Conclusions  
In this study, we proposed a model for building an ontology of a TTS, answering the research question \"How can we develop a tourist traceability ontology based on gathering and processing ubiquitous data, using Big Data techniques?\" The gap demonstrated in the state-of-the-art showed us the lack of an ontology whose domain was tourist traceability. Therefore, we proposed a model for the creation of the OntoTouTra ontology. In turn, we adapted the lifecycle of Big Data analytics presented by [PERSON] et al. [16] to deal with the volume, variety, and velocity of data coming from ubiquitous sources, in particular from an OTA.  
We applied the GQM approach of the FOCA methodology to validate the OntoTouTra ontology and achieved a score of 99.77% of the total quality of the ontology. We used HermiT, Protege, and OOPS! as evaluation tools. However, the number of individuals in the ontology, especially tourist reviews, required enormous computational resources. For instance, we used HermiT as a Protege [56] plugin, and the capacity of this tool restricted its execution. For the evaluation tests, we had to ignore the individuals of the tourist reviews. A new research challenge arises to adapt this type of ontological tool to Big Data environments. The amount of knowledge affects the quality of the ontology's testing processes, which is imperative in this environment. The analysis of the ontology validation results demonstrated its functionality. The validation was conceptual, whose aim was to evaluate the purpose and functionality of the ontology. This goal was achieved by executing SPARQL queries for 10 KPIs representative of a TTS.  
As contributions of this study, we highlight the construction model of the ontology, the adaptation of the lifecycle of Big Data analytics so that the ontology works with ubiquitous data sources in Big Data contexts, and the interoperability of the ontology with open systems, since it allows SPARQL queries and RESTFUL API. The source code allowed the creation, access, and use of the ontology in Big Data environments, using PySpark, and the provision of the ontology for open link data, in particular with GeoNames and Time Ontology. The results of this study are a meaningful contribution to the scientific community and to DMOs looking for a knowledge base to support decision-making regarding destination management.  
The practical application of the developed ontology is extensive: it serves as a knowledge base to support decision-making in the destination, recommendation systems for tourist experiences, monitoring of the management of the DMO, the design or improvement of tourist experiences, the benchmarking of tourist experiences, tourist service providers, and web portals on destination tourist information, among others.  
Through the OntoTouTra ontology, we plan to consolidate the knowledge base for DMOs. As future work, we will include other ubiquitous computing sources, such as data from tourist mobile devices and sensors from POIs. Besides, we will offer a portfolio of tourist experiences of the destination.  
The following are available at www.mdpi.com/xxx/s1.  
Conceptualization, J.F.M.-M., L.S.-G., A.F.V. and G.R.-G.; data curation, J.F.M.[PERSON]; formal analysis, [PERSON]; funding acquisition, [PERSON]; investigation, [PERSON]; methodology, [PERSON] and [PERSON]; project administration, [PERSON]; software, [PERSON]; supervision, [PERSON] and [PERSON]; validation, [PERSON], [PERSON], A.F.V. and [PERSON]; visualization, [PERSON] and [PERSON]; writing--original draft, [PERSON]; writing--review and editing, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This paper was funded by the Universidad del Cauca (501100005682).  
This research was financially supported by the Ministry of Science, Technology, and Innovation of Colombia (733-2015) and by the Universidad Santo Tomas Seccional Tunja.  
The authors declare no conflict of interest.