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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/111623


    Title: Effectively mining time-constrained sequential patterns of smartphone application usage
    Authors: Hsu, Kuo-Wei
    徐國偉
    Contributors: 資訊科學系
    Keywords: Data mining;Information management;Signal encoding;Smartphones;Application developers;Log mining;Mobile Technology;Sequential pattern mining algorithm;Sequential patterns;Smart-phone applications;Technical details;Usage patterns;Filtration
    Date: 2017-01
    Issue Date: 2017-08-03 14:12:58 (UTC+8)
    Abstract: With the rapid development of mobile technology, today users have the freedom to download, install, and use various applications on smartphones. For system and application developers, it is certainly advantageous to better understand how users use the applications on their smartphones. To achieve this, we perform pattern mining on real-world data collected from tens of smartphone users for several years. We aim to mine the sequential patterns each of which satisfies a constraint on the maximum time interval between two adjacent application uses. However, we cannot mine all such patterns by first using the time constraint to filter the data and then using a general sequential pattern mining algorithm, neither can we do so by using the stateof-the-art implementation of the algorithm dedicated to mine sequential patterns satisfying the time constraint. In this paper, we present a solution to the problem of mining time-constrained sequential patterns with technical details, and we present the results that will be beneficial to the related studies. © 2017 ACM.
    Relation: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication, IMCOM 2017,
    11th International Conference on Ubiquitous Information Management and Communication, IMCOM 2017; Beppu; Japan; 5 January 2017 到 7 January 2017; 代碼 126221
    Data Type: conference
    DOI link: http://dx.doi.org/10.1145/3022227.3022265
    DOI: 10.1145/3022227.3022265
    Appears in Collections:[Department of Computer Science ] Proceedings

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