Abstract
As network-oriented pattern identification has become more popular in practical applications, tabbed browsing analysis as an effective method in service computing, deserves more attention. The session identification plays an important role to achieve tabbed browsing. However, the fixed threshold session identification scheme in the field of the session may achieve unsatisfactory performance due to the inappropriate division for sessions in some cases. To avoid such limitation, a novel session identification scheme is proposed in this article. Considering the different operation practices in tabbed browsing, our projected scheme designs an effective scheme through the use of different segmentation thresholds for different users. We present the detailed design algorithm to explain, how effective optimization can be achieved in this novel scheme. Moreover, we test the effectiveness of our scheme on two large-scale data sets from the computing environments in NASA and USTB to demonstrate its optimization performance.
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Acknowledgement
This work is funded by the National Key Technologies R&D Program of China under Grants 2013BAI13B06 and 2015BAK38B01, and the Fundamental Research Funds for the Central Universities under Grant 06500025.
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Yonghong, X., Yifei, W., Dezheng, Z. (2017). A Novel Session Identification Scheme with Tabbed Browsing. In: Xhafa, F., Patnaik, S., Yu, Z. (eds) Recent Developments in Intelligent Systems and Interactive Applications. IISA 2016. Advances in Intelligent Systems and Computing, vol 541. Springer, Cham. https://doi.org/10.1007/978-3-319-49568-2_22
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DOI: https://doi.org/10.1007/978-3-319-49568-2_22
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