Despite the advent of wearable devices and the proliferation of smartphones, there still is no ideal platform that can continuously sense and precisely collect all available contextual information. Mobile sensing data collection approaches should deal with uncertainty and data loss originating from software and hardware restrictions. We have conducted lifelogging data collection experiments from many users and created a rich dataset (7.5 million records) to represent the real-world deployment issues of mobile sensing systems. We create a novel approach to identify human behavioral motifs while considering the uncertainty of collect data objects. Our work benefits from combinations of sensors available on a device and identifies behavioral patterns with a temporal granularity similar to human time perception. Employing a combination of sensors rather than focusing on only one sensor can handle uncertainty by neglecting sensor data that is not available and focusing instead on available data. Moreover, we demonstrate that using a sliding window significantly improves the scalability of our analysis, which can be used by applications for small devices such as smart phones and wearable.