A fog computing based concept drift adaptive process mining framework for mobile APPs

Tao Huang, Boyi Xu, Hongming Cai, Jiawei Du, Kuo-Ming Chao, Chengxi Huang

    Research output: Contribution to journalArticlepeer-review

    5 Citations (Scopus)

    Abstract

    Mobile applications are widely used to provide users convenient and friendly service experiences. Meanwhile, service logs generated by mobile applications are analyzed to obtain user behavior patterns for monitoring and optimizing mobile application performances. However, due to the frequent updates in mobile application, situations of concept drifts often occur in service log streams, which lead to challenges in mobile process mining. In this paper, a novel framework is proposed to solve the above problems by combining fog-computing-based concept drift detecting with cloud-computing-based process mining. Firstly, incomplete log data are preprocessed using fog-computing technologies to provide more accurate log contexts and lower overhead. Then, concept drift detecting methods are used in cloud computing layer to deal with the transfer of mobile applications from one version to another.
    Original languageEnglish
    Pages (from-to)670-684
    Number of pages15
    JournalFuture Generation Computer Systems
    Volume89
    Early online date23 Jul 2018
    DOIs
    Publication statusPublished - 1 Dec 2018

    Keywords

    • Process mining
    • Concept drift
    • Fog computing
    • Log analysis
    • Cloud governance

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