Context-aware rule learning from smartphone data: survey, challenges and future directions

Abstract

AbstractSmartphones 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 analyticsand building data-drivencontext-aware systemshave 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 ofcontext-aware rulesfrom smartphone data is the key. This requires advanced data analytical techniques with high precision andintelligent decision makingstrategies based on contexts. In comparison to traditional approaches,machine learningbased techniques provide more effective and efficient results for smartphone data analytics and corresponding context-aware rule learning. Thus, this article first makes asurveyon previous work in the area of contextual smartphone data analytics and then presents a discussion ofchallengesandfuture directionsfor effectively learning context-aware rules from smartphone data, in order to build rule-based automated and intelligent systems.