Focusing on the main research context represented by the issue of supporting big data analytics over big graph data, this paper introduces and experimentally assesses the framework MALAGA (MultidimensionAL Big DAta Analytics over Massive Graph DAta). MALAGA incorporates several innovations, including OLAP analysis of big graph data, columnar-OLAP methodologies, and Apache Hive extensions. A comprehensive experimental assessment and analysis of the framework’s performance is finally presented and discussed, by significantly integrating the conceptual contributions of our research.

This paper proposes a new theory on combining innovative Multidimensional Big Data Analytics with well-known Machine Learning (ML) in order to magnify the expressive power and the accuracy of knowledge insights discovery from massive big datasets. At the level of enabling technology, with the goal of fully supporting this novel paradigm, the issue of managing and mining big OLAP data cubes over Clouds arises. Due to computational complexity requirements, the latter challenge is addressed by proposing an innovative solution for (1) representing big OLAP data cubes over Clouds via a multi-column-based representation, and (2) compressing the deriving multi-column representations for achieving the desired effectiveness and efficiency. This paper introduces the fundamental model of Machine-Learning-Based Multidimensional Big Data Analytics, along with a reference architecture implementing it.