• RICERCANDO - MONROE - Rapid Interpretation and Cross-Experiment Root-Cause Analysis in Network Data with Orange
The Client : Evropska komisija ( RICERCANDO - MONROE )
Project type: European projects
Project duration: 2016 - 2018
  • Description

The popularity of mobile devices like smartphones and tablets, combined with the always everywhere Internet connectivity provided by mobile broadband (MBB) networks, has radically changed the way most people live and work. Due to the increasing importance of MBB network infrastructures in society and economy, there is nowadays a compelling need to gather objective information about their performances. Such information is very valuable for many parties including operators, regulators and policy makers, consumers and society at large, businesses whose services depend on MBB networks, researchers and innovators. Within this motivating framework, the EU project MONROE (Measuring Mobile Broadband Networks in Europe) will design, deploy and operate the first European transnational open platform for independent, multi-homed, large-scale monitoring and assessment of performance of MBB networks (https://www.monroe-project.eu). The MONROE platform supports several independent experiments and smaller projects, including RICERCANDO (Rapid Interpretation and Cross‐Experiment Root-Cause Analysis in Network Data with Orange), an independent project run at the Faculty of Computer and Information Science of the University of Ljubljana. The goal of the RICERCANDO project is to develop an advanced toolbox for mining MONROE data to support integrative exploration, visualization and interpretation of data and meta-data across multiple experiments. The integration of these data with advanced data mining and interactive data exploration features will support the human experts in the process of detecting and understanding the root-cause of the network problems and performance degradations. A distinguishing feature of RICERCANDO is the interdisciplinary composition of the project team that includes established data mining experts (prof. Zupan) working together with networking experts (prof. Ricciato, prof. Pejovic).