Generating dynamic higher-order Markov models in web usage mining

Markov models have been widely used for modelling users’ web navigation behaviour. In previous work we have presented a dynamic clustering-based Markov model that accurately represents second-order transition probabilities given by a collection of navigation sessions. Herein, we propose a generalisation of the method that takes into account higher-order conditional probabilities. The method makes use of the state cloning concept together with a clustering technique to separate the navigation paths that reveal differences in the conditional probabilities. We report on experiments conducted with three real world data sets. The results show that some pages require a long history to understand the users choice of link, while others require only a short history. We also show that the number of additional states induced by the method can be controlled through a probability threshold parameter

EXCISION OF THE SUPERIOR TARSUS AND CONJUNCTIVA IN THE TREATMENT OF TRACHOMA

Latency is a fundamental problem for all distributed systems including digital libraries. To reduce user perceived delays both caching -- keeping accessed objects for future use -- and prefetching -- transferring objects ahead of access time -- can be used. In a previous paper we have reported that caching is not worthwhile for digital libraries due to low re-access frequencies. In this paper we evaluate our previous findings that prefetching can be used instead. To do this we have set up an experimental prefetching proxy which is able to retrieve documents from remote fulltext archives before the user demands them. Using a simple prediction to keep the overhead of unnecessarily transfered data limited, we find that it is possible to cut the user perceived average delay a factor of two

An Overview of Web Data Clustering Practices

Clustering is a challenging topic in the area of Web data management. Variou