Exploiting structure and concepts for enhanced management of information and networked systems

The need for access to information and services in modern societies has fuelled the rapid growth of networks and web technologies. The increasing complexity of modern networks and the overwhelming amount of available information require radical changes in network and information management approaches. One of the prerequisites for successful management of the resultant complex networks is the ability to handle large amounts of information. Information management and retrieval systems are also extremely important from a purely business and corporate perspective. One of the fundamental problems with most current information management systems relates to identifying suitable ways for representation and inference of concepts and the exploitation of these concepts and structure by the management systems. In general, the information may contain incoherent, missing, or unreliable data that need to be filtered and processed. In this respect, artificial intelligence techniques offer many appealing solutions. In particular, the power of soft computing in handling uncertainties makes it an appropriate choice for taking up a significant role in information handling and management of networked systems. This paper focuses on this topic, focusing on the role of fuzzy proximity networks and multiple distributed agents in identifying or improving solutions to information and network management problems. This work also highlights how structural concepts can be used in enhancement of information and network management methodologies. To demonstrate the effectiveness of this approach, some specific areas of functional importance, with emphasis on information retrieval and management are also discussed.