# Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data
## 3 Considerations on Data Mashup
### Database Management Technology  
Building a data mashup greatly depends on the DataBase Management System (DBMS) technology data providers use. On the one hand, relational DBMSs are still prevailing to implement internet information systems (72.8% score according to _DB-Engines_' ranking, available at [[https://db-engines.com/en/ranking_categories](https://db-engines.com/en/ranking_categories)]([https://db-engines.com/en/ranking_categories](https://db-engines.com/en/ranking_categories)), accessed on 9 September 2021). Hence, basing our data mashup implementation upon relational databases would have been reasonable. However, the popularity of graph databases is constantly increasing [39]--graph DBMS are 14 times more prevalent than relational DBMSs. On the other hand, opting for general-purpose graph database technology would require revised versions of PPDP algorithms, which is out of our scope.  
An option to share the data mashup schema is using an LDP-compliant triplestore. Despite its currently low popularity (0.4% ranking score), RDF triplestores are graph databases that enable sharing schemata through ontologies and vocabularies. Besides, RDF triplestores are usually implemented on top of relational DBMS or graph databases [40], making LDP an acceptably interoperable solution to publish and share different providers' data schemata, either relational or graph-based. Another choice would have been to use the GraphQL Schema Definition Language (SDL) (available at [[https://graphql.org/learn/schema](https://graphql.org/learn/schema)]([https://graphql.org/learn/schema](https://graphql.org/learn/schema)), accessed on 9 September 2021) to define both the providers' schema and the data mashup schema. Using Linked Data or GraphQL is an implementation decision that does not affect the validity of the mashup protocol proposed below.  
As exchanging the data providers' schemata depends on their implementation choice of DBMS technology, we need an independent means to share the information required by the mashup protocol. We opt for using the Web of Data standards for representing the data mashup, while data providers' vertical partitions are represented as relational tables, as explained next. The choice of relational DMBS as the source of data is supported by the scarce availability of VLE datasets as another format than relational database dumps.  
To illustrate the mashup protocol in a real e-learning mashup example, we will use the Open University Learning Analytics Dataset (OULAD) [62], formed by several relational data tables, each concerning different aspects of students' activity in a LMS. Although OULAD is actually a monolithic data dump, it is structured as three parts: (i) student demographics, which contains demographic information about the students together with their results; (ii) student activities, which contains the results of students' assessments and information about the time when the student registered in modules; and (iii) course module presentations, which contains information about available course modules, assessments, and materials. Each OULAD part is considered to be stored by a separate data provider as a more realistic cloud-based, personal learning environment [7] that might involve two or more educational institutions as data providers [11].