## 2 Materials and Methods
### Data Analysis Model
#### 2.2.1 Data Analysis Architecture  
The data analysis architecture used is defined in four interconnected stages. In Figure 3, see each of the stages and their components. The first stage of the architecture are the data sources that are considered in the analysis. These sources are the databases available to the university participating in this research and which store the information generated in the academic and financial part. These sources are important to determine the student's situation regarding her performance and if this has any inconvenience, the framework crosses the data from all sources to determine where the problem originates. Other sources are sensor and actuator systems [42]. The information they generate is specifically from the interaction of students with the university campus. This information is important considering the return of students to campus. The data from these sources are generatedby different sensors that are responsible for identifying certain patterns in students such as the places they access, through biometrics, radio frequency identification, video cameras, etc. Other sources that are important today for their contribution to detecting trends are social networks [43]. Through these, it is possible to determine the acceptance of academic programs or the states that the different careers climb in future sentiment analyses. The second stage is in charge of the place where the data is stored and its format. There is a great variety of data in a university; these will depend on the type of devices or sources [44]. Generally, the services that a university has are stored in its own data center. These, when managing a virtualization model, can generate as many servers as their infrastructure allows. The services store their data in databases within local storage [45]. In addition, there are services that store your data in private or public cloud models. Its implementation depends on its use, for example, the university maintains a private cloud, which is responsible for storing files that are shared between different users [46]. In the public cloud, services such as Office 365 and various Internet of Things (IoT) device measurement systems are maintained that the university has deployed for the interaction of the campus with the user [47]. In the knowledge stage, the Hadoop framework is deployed, which takes care of all the data processing. Hadoop is an open-source environment from Apache that allows the data analysis architecture to be highly distributed, functional, and scalable, without depending on the investment of licenses or hardware [48]. It uses a Java programming language that has several additional applications that empower it, in addition to its own adaptation of the MapReduce programming model for processing large volumes of data, including structured and unstructured [49]. Hadoop is designed to process large volumes of data in a distributed manner through MapReduce. In addition, thanks to the fact that it works with local, but distributed storage and processing, it allows working with both single-node clusters and thousands of nodes, offering a high level of fault tolerance. It consists mainly of two components: data storage/distribution and data processing [50]. * Hadoop Distributed File System (HDFS), is a distributed file system which allows data to be spread through hundreds or thousands of nodes for processing. It provides redundancy, that is, the data is repeated or replicated in several nodes [51]. In addition, it provides fault tolerance: if a node fails, it is automatically replaced. In its operation, the HDFS file system divides the data into blocks, where each block is replicated in different nodes so that the fall of a node implies the loss of the data it contains [52]. Figure 3: Big Data architecture, with the use of a Hadoop framework on a university campus [39].