AMACS: Automated Mobile Application Content Sensing

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.