Mintra: Mining anonymized trajectories with annotations

Time-series of geo-tagged data are routinely generated from GPS enabled devices, satellites and other motion capturing instruments. Such data can be thought of as sequences of locations where every location is associated with additional text annotations. Pattern mining for important sequences (aka. trajectory mining) is essential to extract information from such a database. However, the current trend of anonymization to avoid privacy breach makes it difficult to identify any correlation in the data, thus making it even harder, if not impossible, to look for actual trajectories. Noting this difficulty, we define our goal as mining for trajectory-patterns which is a generalization of trajectories. We first design a pattern-growth based algorithm towards this objective. Further, by identifying the limitation of the state-of-the-art sequential pattern growth algorithms in growing trajectory-patterns, we propose a new pattern growth algorithm-- Mintra. Experiments were performed to demonstrate efficiency and effectiveness of Mintra. We, therefore, show that important patterns can be mined from anonymized data without compromising user privacy.