Data Mining for Monitoring and Managing Systems and Networks

Abstract

Modern communication networks have reached a level of scale, dynamics and complexity that demands for sophisticated management and control, driving many researchers towards the investigation of computational intelligence in the particular context of networks [1, 2]. This is a difficult proposition, which requires a crossdisciplinary approach, pulling together efforts from a variety of research communities. During the last few decades we have seen substantial advances in topical areas such as statistical analysis, autonomic computing, machine learning and data mining, to master the significance of massive data sets. However, the use of such techniques in the context of communication networks has been investigated only recently. To capture relevant work-in-progress, in 2011 we started the IEEE ICDM Workshop on Data Mining in Networks (now at its third edition, http://damnet. reading.ac.uk/), to facilitate a rich exchange of ideas among computer scientists, network experts and life scientists, who share a common interest in extracting models and information from any kind of complex networks (both natural and manmade). Encouraged by the outcomes of DaMNet we decided to create this Journal Special Issue, which has a more specific focus on the application of data mining to communication networks and addresses open issues in network control and management. Overall we received thirteen submissions, covering a wide range of topics, theories, applications, analytical models and simulations. We started a rigorous peer-review process that was not free from controversies and conflicting reviews. We discovered how difficult it is to evaluate manuscripts that sit at the intersection