Authors: Uroš Jovanovič; Aleš Štimec; Daniel Vladušič; Gregor Papa; Jurij Šilc
Addresses: XLAB d.o.o., Pot za Brdom 100, SI-1000 Ljubljana, Slovenia ' XLAB d.o.o., Pot za Brdom 100, SI-1000 Ljubljana, Slovenia ' XLAB d.o.o., Pot za Brdom 100, SI-1000 Ljubljana, Slovenia ' Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia ' Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia
Abstract: 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.
Keywords: data mining; big data analytics; data-centric; parallelisation; visualisation; multi-resolution analysis; array databases; architecture; anomaly detection; complex relationships; complex processes.
International Journal of Business Intelligence and Data Mining, 2015 Vol.10 No.4, pp.337 - 355
Available online: 04 Oct 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article