Smartphones are considered as one of the most essential and highly personal devices of individuals in our current world. Due to the popularity of context-aware technology and recent developments in smartphones, these devices can collect and process raw contextual data about users’ surrounding environment and their corresponding behavioral activities with their phones. Thus, smartphone data analytics and building data-driven context-aware systems have gained wide attention from both academia and industry in recent days. In order to build intelligent context-aware applications on smartphones, effectively learning a set of context-aware rules from smartphone data is the key. This requires advanced data analytical techniques with high precision and intelligent decision making strategies based on contexts. In comparison to traditional approaches, machine learning based techniques provide more effective and efficient results for smartphone data analytics and corresponding context-aware rule learning. Thus, this article first makes a survey on previous work in the area of contextual smartphone data analytics and then presents a discussion of challenges and future directions for effectively learning context-aware rules from smartphone data, in order to build rule-based automated and intelligent systems.

中文翻译：

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从智能手机数据中学习上下文感知规则：调查，挑战和未来方向

智能手机被认为是当今世界上个人最重要，最高度个人化的设备之一。由于上下文感知技术的普及和智能手机的最新发展，这些设备可以收集和处理有关用户周围环境及其手机相应行为活动的原始上下文数据。因此，近几年来，智能手机数据分析和构建数据驱动的上下文感知系统受到了学术界和工业界的广泛关注。为了在智能手机上构建智能的上下文感知应用程序，从智能手机数据中有效学习一组上下文感知规则是关键。这需要高精度的高级数据分析技术，并且基于上下文的智能决策策略。与传统方法相比，基于机器学习的技术为智能手机数据分析和相应的上下文感知规则学习提供了更有效的结果。因此，本文首先对上下文智能手机数据分析领域中的先前工作进行了调查，然后提出了从智能手机数据中有效学习上下文感知规则的挑战和未来方向的讨论，以构建基于规则的自动化和智能系统。