Distributed framework

Efficient join processing plays an important role in big data analysis. In this work, we focus on generic theta joins in a massively parallel environment, such as MapReduce and Spark.

We deal with binary theta-joins in a MapReduce environment, and we make two contributions. First, we show that the best known algorithm …

Collaborative recommenders rely on the assumption that similar users may exhibit similar tastes while content-based ones favour items that found to be similar with the items a user likes. Weak related entities, which are often considered to be useful, are neglected by those similarity-driven recommenders. To take advantage of this neglected information, we introduce a novel dissimilarity-based recommender that bases its estimations on degrees of dissimilarities among items’ attributes.