PARADISE - PARAllel and DIStributed Evolutionary agent-based systems for machine learning and big data mining
Research areas concerning the development of hardware and software frameworks aiming at searching for regularities in massive datasets (big data mining) recently gained a strategic role, with remarkable impacts in different fields.
The objectives of the PARADISE project are:
1) Development and implementation of a distributed multi-agent clustering algorithm (hereinafter LS-EABC ¿ Large Scale Evolutive Agent Based Clustering) and its variant for solving classification problems (hereinafter SLS-EABC ¿ Supervised Large Scale Evolutive Agent Based Classifier), both based on evolutive optimization and designed for a full exploitation of massively parallel (i.e. multicore) hardware systems. Moreover, both the algorithms will be capable of data processing in non-metric spaces and will be conceived for performing Local Metric Learning, identifying the subsets of relevant characteristics in which significant clusters exist.
2) Design and realization of a Distributed Computing Platform (hereinafter PDCP) based on 8 low-cost devices (Parallella Board), each one equipped with a 16 physical cores co-processor (128 total physical cores).
3) Test and comparison between PDCP and a workstation (hereinafter XPW) equipped with an Intel Xeon Phi CPU, in running LS-EABC and SLS-EABC.
4) Application of LS-EABC and SLS-EABC on three different relevant topics, aiming at showing the high flexibility of the proposed system. Specifically:
a) Fault detection and classification of medium-voltage power lines faults for predictive maintenance systems in Smart Grids.
b) Real-time identification of security attacks in Wi-Fi networks.
c) Mining metabolic networks for charactering pathological gut flora mixture aimed at precision medicine.