AMACS: Automated Mobile Application Content Sensing
AMACS: Automated Mobile Application Content Sensing
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DOI：
10.1109/tcss.2019.2962104
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发表时间：
2020-02
影响因子：
5
通讯作者：
Zexun Jiang;Hao Yin;Yan Luo;Jiaying Gong
中科院分区：
文献类型：
--
作者：
Zexun Jiang;Hao Yin;Yan Luo;Jiaying Gong
经过十年的快速发展，移动设备已成为人们日常生活的重要组成部分，移动应用产生丰富的内容并提供重要的信息访问。移动应用程序内的内容可以为社交感知和应用程序开发提供有价值的见解。然而，与网络搜索不同的是，缺乏有效的方法来感知和索引来自移动应用程序的信息。本文提出了一种自动化移动应用内容感知（AMACS）框架，它可以成为有效提取移动应用内容并进行测量的基本工具。 AMACS 使用内容感知发现模型从各种移动应用程序中有效提取内容，无需任何手动干预。其模块化设计具有独立的构建块，可以轻松扩展为分布式系统以进行大规模部署。 AMACS与现有的相关移动自动化分析方法进行比较，以评估其性能。结果表明，爬虫能够以较低的开销有效地覆盖移动应用中的更多内容，并且可用于社交感知和网络测量等应用场景的内容提取。
After a decade of rapid development, mobile devices have become an essential part of people’s daily life and mobile applications generate rich contents and provide important information access. Contents inside mobile applications can provide valuable insights for social sensing and application development. However, unlike Web search, there is a lack of efficient methods to sense and index information from mobile applications. This article proposes an automated mobile application content sensing (AMACS) framework, which can be the fundamental tool to extract effectively the contents and conducting measurements in mobile applications. AMACS uses a content-aware discovery model to extract effectively the contents from a variety of mobile applications without any manual intervention. Its modular design with independent building blocks can be readily expanded into a distributed system for large-scale deployment. AMACS is compared with the existing relevant mobile automated analysis methods to evaluate its performance. The results show that the crawler can cover more contents efficiently in mobile applications with low overheads, and it can be used to extract contents for application scenarios like social sensing and network measurements.