As corporate data is more and more becoming an invaluable asset for decision makers, technologies such as data warehouses and data mining are an essential part of a company's information infrastructure. Methods for mining corporate data and extracting rules from it are well established for crisp and traditional fuzzy data. This paper aims at extending the traditional fuzzy approach for situations where membership values are intervals.

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PurposeThe paper seeks to improve website performance by developing a specific preloading strategy tuned to the needs of a web server. Applying simple preloading strategies, as used in various operating systems' memory management algorithms, does not suffice when managing websites due to the uncertainty of internet users' behaviour.Design/methodology/approachThis paper uses rough, or fuzzy, sets as the framework to introduce a website management strategy based on a user's stated preference. This mathematical approach allows the derivation of preloading strategies from uncertain and partially contradicting information generated from site usage statistics.FindingsA paper example of an application of the algorithm is used to illustrate how this approach can be applied to efficiently manage a website.Originality/valuePerformance is one of the key issues in managing websites, especially as the internet gains popularity and becomes the common access point for information retrieval. This technique has the potential to deliver greater efficiency; faster response times; and reduces the need to hold detailed individual user profiles.

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Many decision problems can be characterized by a set of possible states and a cost associated with each possible state transition. In this paper we discuss how to select a policy from a set of possible policies in the long term. If the cost matrix is not available the transition matrix can be used to compare expected return times to states. In our setting the transition matrix is defined by use of linguistic terms and as a consequence, the expected return times are fuzzy. In the case where the cost matrix is available, fuzzy average costs are computed. The resulting fuzzy quantities are compared by introducing the concept of minimizing sets. Finally, we look at the case where the transition takes place from a state to a state that is known to be an element of some subset of states, but we do not know which one. We use the Dempster-Shafer theory [Shafer 19761 together with techniques of Norton [Norton 19881 and Smetz [Smetz 19761 to approximate the transition probabilities.