In modern Economies, knowledge management systems (KMSs) applications are gradually adopted from a growing number of enterprises, organizations and governments. As digital content availability is increasing dramatically through centralized of distributed digital libraries operation, a great research interest is developed upon ''clever'' knowledge retrieval based on each user's individual preferences. Modern man usually requests to get knowledge under time pressure. In many cases it is not possible to have enough time to evaluate extensively the huge amount of results presented after a ''string-based'' criteria query to a digital library. Next generation KMSs should be able to eliminate the results of a search query, based on a certain user's profile. This profile carries information about the level of preexistent knowledge that maybe a user has on a certain knowledge area, the exact scope of its research and the time that he has available in order to exploit the results of this research. In this paper a new generation of KMSs is proposed that are supporting personalized knowledge retrieval. This is achieved through an innovative architecture of enriched knowledge objects for knowledge representation and the development of an expert system for diagnosis and dynamic knowledge composition.

PurposeThis paper aims to deal with disaster and recovery systems, introducing the catastrophe matrix as a disaster‐preventing tool.Design/methodology/approachA new model is proposed which is proved to be a classical assignment problem. It is solved based on well‐known optimization methods. Research is presented for the danger risk of the system and a fully automated recovery system with a minimum cost is presented.FindingsResearch accepts the fact that a catastrophe has already been activated on the system.Research limitations/implicationsThe difficulty of solving the proposed model of the threat and countermeasures still remains.Originality/valueThe proposed models show a new original way to confront dangers and overcome the existing classical models based on risk management.

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