# OntoTouTra: Tourist Traceability Ontology Based on Big Data Analytics
## 1 Introduction  
The relationship between the concept of traceability and the tourist contributes to the improvement of the methodological approaches used in studies because it provides us with the precision and validity of the data obtained, especially from ubiquitous environments [1]. Traceability constitutes an advance for the collection of tourist mobility data in spatial-temporal relationships. Traditionally, in the fields of production, logistics, and software, traceability has been considered as the set of actions, metrics, and technical procedures to identify and record each product from the beginning to the end of the supply chain [2]. Furthermore, the ISO defines the traceability concept \"as the ability to trace the history, application, or location of that which is under consideration.\" [3]. Furthermore, the GS1 defines tracing as \"the ability to identify the origin, attributes, or history of a particular traceable item\" and tracking as \"the ability to follow the path of a traceable item\" [4]. In this sense, through a TTS, the DMO can identify the routes of the tourists and the degree of interest that the attractions of the destination arose in them. Furthermore, TTS can use sociodemographic metrics and statistics reports to identify tourist profiles, to prepare and adapt both the tourist destination and the tourism management system. Hence, with the accelerated technological advance that characterizes ubiquitous computing, nowDMOs have at their disposal various data sources. These sources provide input data for the TTS, such as social networks, cloud platforms, the web, the IoT, traditional databases, public or private datasets, and linked data, among other data sources. On the other hand, these data sources typically are extensive volume datasets and reach high speed (in real time or almost in real time). Furthermore, variety is another characteristic of these data (some have a format; the vast majority do not). Big Data can process and store this type of data and constitute a knowledge base through ontological systems. In this way, the DMO can make decisions based on the information processed. Currently, in most cases, the DMO makes decisions based on paper surveys applied to some tourists. Furthermore, government reports and those of the tourism sector actors serve as data for this decision-making process. These strategies have drawbacks, such as the subjectivity and predisposition of tourists to answer surveys. Many of them prefer not to answer them for time or data privacy reasons, and government reports are generated in extended periods, and in some cases, they arrive late. For this reason, the research gap of this study arises, which takes advantage of data from ubiquitous sources to provide information related to the traceability of tourists to a given destination. In this way, with the processing of these characteristic Big Data, precisely due to the volume, velocity, and variety, to constitute a knowledge base, the research question arises: How can we develop a tourist traceability ontology based on obtaining data and ubiquitous data processing, using Big Data analytics techniques? It is worth mentioning that the purpose of this study is to constitute an ontology based on data previously generated in a massive way, not on data from tourists in particular. Initially, we considered the data from three types of ubiquitous sources: reviews of tourists in OTAs, data from sensors located in the POIs of the destination, and data from tourist guide applications installed on the tourist's mobile devices, which have prior permission for further processing. A tourist traceability ontology allows the DMO to make decisions regarding the management of the destination according to the flow and track of tourists, determine their preferred POIs, intelligently dispose of the infrastructure for adequate attention, and foresee improvements in services, as well as design tourist experiences according to the interests of the tourist in a space-time causality.