In order to make on-line topic detection more efficient, a new method is proposed based on named entity recognition. New method extracts news elements from stories. Based on news elements, query composition is used to detect story link. This process reduces complex computation of text similarities. Experimental result indicates that the proposed method performs on-link topic detection accurately and efficiently.

Even XML is used as a popular data exchange standard over Internet and Intranet, its space expansion makes the transmitting and storing of XML data very expensive in terms of resources because of adding tags to every different semantic content unit. After compressed, its size will be much smaller, but how to evaluate query efficiently and directly based on the compressed data is still a necessary work. The authors propose an XML index structure using B+ tree as its' backbone structure, on compressed data which is resulted from revert arithmetic compression, ArithRegion. Queries as the form of //element₁/element₂/…/elment_m can be evaluated efficiently using ArithRegion.

Mining moving sequential patterns has great significance for effective and efficient location management in wireless communication systems. Mining moving sequential patterns is different from mining conventional sequential patterns, firstly it needs to consider much about the time factor in moving sequences; secondly it cares about what the next moving is for mobile user, so items must be successive in mining moving sequential patterns. A novel technique to mine moving sequential patterns is proposed. A clustering method is introduced to preprocess the original moving histories into moving sequences, whose main role is to discretize the time attribute. And then an efficient method, called PrefixTree, is presented to mine the moving sequences. Performance study shows that PrefixTree outperforms Revised PrefixSpan-2, which is revised to mine moving sequences, in mining large moving sequence databases.