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In this work, we propose a new approach for discovering various relationships among keywords over the scientific publications based on a Markov Chain model. It is an important problem since keywords are the basic elements for representing abstract objects such as documents, user profiles, topics and many things else. Our model is very effective since it(More)
In this paper, we introduce and study an efficient regular queries processing algorithm on a very large XML data set which is fragmented and stored on different machines. The machines are connected by the high speed interconnection. In this system the efficiency of a query processing algorithm depends on two main factors: the waiting time for the answer and(More)
There are two main topics in this paper: (i) Vietnamese words are recognized and sentences are segmented into words by using probabilistic models; (ii) the optimum probabilistic model is constructed by an unsupervised learning processing. For each probabilistic model, new words are recognized and their syllables are linked together. The syllable-linking(More)
Ranking has been applied in many domains using recommendation systems such as search engine, e-commerce, and so on. We will introduce and study N-linear mutual ranking, which can rank n classes of objects at once. The ranking scores of these classes are dependent to the others. For instance, PageRank by Google is a 2-linear ranking model, which ranks the(More)
The orthogonal field components from global IN-TERMAGNET magnetometer stations are studied via multi-fractal detrended fluctuation analysis to determine whether there are clear and consistent regional patterns in the behavior of the fluctuations. There are three distinct scaling regimes in the qth-order fluctuation function for each of the 24 stations(More)