Platys: An Active Learning Framework for Place-Aware Application Development and Its Evaluation

We introduce a high-level abstraction of location called place. A place derives its meaning from a user's physical space, activities, or social context. In this manner, place can facilitate improved user experience compared to the traditional representation of location, which is spatial coordinates. We propose the Platys framework as a way to address the special challenges of place-aware application development. The core of Platys is a middleware that (1) learns a model of places specific to each user via active learning, a machine learning paradigm that seeks to reduce the user-effort required for training the middleware, and (2) exposes the learned user-specific model of places to applications at run time, insulating application developers from dealing with both low-level sensors and user idiosyncrasies in perceiving places.

We evaluated Platys via two studies. First, we collected place labels and Android phone sensor readings from 10 users. We applied Platys' active learning approach to learn each user's places and found that Platys (1) requires fewer place labels to learn a user's places with a desired accuracy than do two traditional supervised approaches, and (2) learns places with higher accuracy than two unsupervised approaches.

Second, we conducted a developer study to evaluate Platys' efficiency in assisting developers and its effectiveness in enabling usable applications. In this study, 46 developers employed either Platys or the Android location API to develop a place-aware application. Our results indicate that application developers employing Platys, when compared to those employing the Android API, (1) develop a place-aware application faster and perceive reduced difficulty and (2) produce applications that are easier to understand (for developers) and potentially more usable and privacy preserving (for application users).