Using distributed raspberry PIs to enable low-cost energy-efficient machine learning algorithms for scientific articles recommendation

In recent decades, machine learning has become a crucial factor in terms of automating business operations and assisting in the decision-making process within an organization. With the huge volume of data generated at an unprecedented rate has motivated researchers and industry analysts to constantly develop effective and efficient analytical models machine learning techniques. This study adds to the data mining community by evaluating some of the most significant text mining techniques and presenting a predictive model that will supposedly ease the process of literature review for researchers. In addition, it compares the execution of the model in terms of cost, energy consumption, accuracy and scalability in three different environments, namely, google cloud instance, google cloud functions and distributed raspberry PIs. Results yielded in our study showed that distributed Raspberry PIs can have a highly positive impact in terms of lowering costs and being energy efficient. On one hand, we found out that machine learning algorithms can be adapted and run on distributed raspberry PIs with low cost and low energy consumption compared to cloud alternatives. On the other hand, this solution does not offer great scalability and it requires more time on management, deployment and configuration. The distributed Raspberry PIs also showed bad performance on execution time compared to the other alternatives when comes to high processing power.