178 Manolopoulos

During the past years, we have witnessed an explosive growth in our capabilities to both generate and collect data. Advances in scientific data collection, the computerization of many businesses, and the recording (logging) of client’s access to networked resources have generated a vast amount of data. Various data mining techniques have been proposed and widely employed to discover valid, novel and potentially useful patterns in these data. Traditionally, the two primary goals of data mining tend to be description and prediction, although description is considered to be more important in practice. Recently though, it was realized that the prediction capabilities of the models constructed by the data mining process can be effectively used to address many problems related to the allocation of resources in networks. For instance, such models have been used to drive prefetching decisions in the World Wide Web (Nanopoulos, Katsaros, & Manolopoulos, 2003) or to schedule data broadcasts in wireless mobile networks (Saygin & Ulusoy, 2002). The intrinsic attribute of these environments is that the network records the characteristics, for example, movements, data preferences of its clients. Thus, it is possible to infer future client behaviors by mining the historical information, which has been recorded by the network. The present article will highlight the data mining techniques that have been developed to achieve efficient allocation of resources, for example bandwidth, to wireless mobile networks or the data mining methods that have been used in order to reduce the latency associated with the access of data by wireless mobile clients.