Big-data analytics: a critical review and some future directions
Uroš J. Jovanović, Aleš Štimec, Daniel Vladušič, Gregor Papa, Jurij Šilc · International Journal of Business Intelligence and Data Mining · 2015
The aim of the paper is to present a critical review of analytics and visualisation technology for big data, and propose future directions to overcome the shortcomings of the current technologies. The current machine learning and data-mining algorithms are operating mostly on predefined scales of aggregation, while in the vast amounts of data the problem arises at the level of aggregation which cannot be defined ahead of time. We therefore identify a novel and extended architecture to operate on flexible multi-resolution hypothesis space. With such architecture framework the goal is to open a space of possibly discovered models towards classes of data, which are by today's approaches discovered only for special cases. Furthermore, the multi-resolution approach to big-data analytics could allow scenarios like semi-supervised and unsupervised anomaly detection, detecting complex relationships from the heterogeneous data sources, and providing ground for visualisation of complex processes.