Comparison of Platforms for Recommender Algorithm on Large Datasets

One of the challenges our society faces is the ever increasing amount of data. Among existing platforms that address the system requirements, Hadoop is a framework widely used to store and analyze "big data". On the human side, one of the aids to finding the things people really want is recommendation systems. This paper evaluates highly scalable parallel algorithms for recommendation systems with application to very large data sets. A particular goal is to evaluate an open source Java message passing library for parallel computing called MPJ Express, which has been integrated with Hadoop. As a demonstration we use MPJ Express to implement collaborative filtering on various data sets using the algorithm ALSWR (Alternating-Least-Squares with Weighted-lambda-Regularization). We benchmark the performance and demonstrate parallel speedup on Movielens and Yahoo Music data sets, comparing our results with two other frameworks: Mahout and Spark. Our results indicate that MPJ Express implementation of ALSWR has very competitive performance and scalability in comparison with the two other frameworks

A conceptual framework for a multi-criteria decision support tool to select technologies for resource recovery from urban wastewater

In the context of circular economy, wastewater can be used to address some of the 21st century's challenges regarding the transition to renewable resources for water, energy, and nutrients. Despite all the research, development, and experience with resource recovery from urban wastewater, its implementation is still limited. The transition from treatment to resource recovery is complex due to the difficulty of selecting unit processes from a large number of candidate processes considering the operational limitations of each process, and sustainability objectives. Presently, a multi-criteria decision support tool that deals with the difficulty of unit process selection for resource recovery from wastewater has not been developed. Therefore, this paper presents the conceptual framework of a decision support tool to find the optimum treatment train consisting of compatible unit processes which can recover water, energy and/or nutrients from a specified influent composition. The framework presents the relationship between the user input, the knowledge library of technologies and a weighted multi-objective nonlinear programming model to aid process selection. The model presented here shows, not only how the processes are selected, but also the four-dimensional sustainability impact of the generated treatment train while considering the weight provided by the user. Thus, this study presents a reproducible framework which can support private and public decision-makers in transparent evidence-based decision making and eventually the systematic implementation of resource recovery from urban wastewater