# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 1 Introduction  
The forecast of a worldwide network following the Internet of Everywhere (IoE) paradigm is currently becoming a reality, mainly thanks to those devices called Smart Objects (SOs). They surely are a part of the Internet of Things (IoT) vision, and they are represented by sensors, smartphones, wearables, tablets and all other equipment allowing for new forms of interaction between the physical world, the things and the people to whom they are attached. Reliable estimates foresee that, by 2020, billions of SOs are expected to be deployed in urban, home, industrial and rural scenarios, in order to collect relevant data, which may be used to build and foster new applications and innovative services for all the involved stakeholders (from citizens to institutions). This huge amount of information, coming from heterogeneous sources, can be framed in the big data paradigm, characterized in terms ofvolume, velocity, variety, veracity and value [1]. Indeed, the most recent big data techniques, which are so popular today mainly due to the spreading of those online and social services they claim to support, attempt at processing extremely large amounts of heterogeneous data for multiple purposes. As a matter of fact, the majority of these techniques has been mainly conceived of to deal with extremely large volumes of data, hence focusing on their amount, rather than on their real-time processing and dispatching. To fill this gap, a new paradigm, alternatively labeled as big data streams or big stream [2], is emerging within those parts of the research community more involved in IoT data traffic. Indeed, this model is more interested in those aspects that, despite being as important as volume, seem to have been somehow forgotten by traditional big data solutions: the data generation rate, the real-time and low latency requirements some consumers have, as well as the management of data flows. Besides this, another concept, namely Fog Computing (FC) [3], is quickly arising as the perfect companion of big data streams: as a matter of fact, the goal of this emerging paradigm is to reduce the amount of data that needs to be transported to the cloud for processing, analysis and storage. Such a technology improves the overall efficiency and real-timeliness, by migrating computing resources, applications and services to the edges of the network. At the time of writing, several educational institutions are relying more and more on the usage of the so-called Virtual Learning Environments (VLEs), mainly fostered by the evolution in the communication bandwidth and in the increased availability (at paltry costs) of the aforementioned pervasive devices, themselves able to provide a connection anywhere and anytime. Thus, both traditional and e-learning-based universities are evolving towards virtual and mobile learning (m-learning) frameworks, thanks to the IoT social revolution and, in the very near future, towards scenarios heavily characterized by the integration of fog and cloud technologies. As a direct consequence of the adoption of these technologies, educational big data mining is emerging as a novel research scenario [4], wherein big data and big data stream mining techniques may be applied to the valuable information coming from different educational data sources, be they either sensor-like gadgets (e.g., wearables) or full-fledged devices (e.g., tablets and laptops). Moreover, these new scenarios allow one to offer a partial solution to the most common legacy issues connected with distance learning environments and educational data mining techniques, such as the requirement for: (i) increasing _scalability_ (considering both infrastructure and algorithms); (ii) _real-time responsiveness_ (for quick responses towards students' requirements and needs); and (iii) _adaptivity_ (since workload peaks may rapidly follow more quiet periods).