Towards next-generation heterogeneous mobile data stream mining applications: Opportunities, challenges, and future research directions
The next revolution in mobile user experience is predicted to be a smart device that can adapt to its user's lifestyle and surroundings to become a proactive personal assistant. We introduce the idea of Mobile Sequence Mining (MSM) engine that automatically learns phone usage sequential patterns over the rich context data captured within the device. The learned patterns can then enable variety of applications including proactive assistance for a variety of use cases. Unlike existing cloud-based intelligence services (e.g., GoogleNow) that rely on internet access and may compromise privacy, MSM provides device intelligence by leveraging mined longitudinal patterns while preserving privacy via on-device mining. MSM is generic and can provide sequential patterns and predictions over multiple data streams, also allowing individual mobile applications to stream their own private data to mine sequential patterns. In our preliminary tests by deploying MSM on 3 user devices, it mines frequent sequential patterns within 8 minutes over 7-53 days of longitudinal user context data including location, app usage and call logs spanning 137-312 unique contexts. We conclude the paper by enumerating future research challenges for mobile sequence mining.