Extracting High-Level Information from Location Data: The W4 Diary Example
Services for mobile and pervasive computing should extensively exploit contextual information both to adapt to user needs and to enable autonomic behavior. To fulfill this idea it is important to provide two key tools: a model supporting context-data representation and manipulation, and a set of algorithms relying on the model to perform application tasks. Following these lines, we first describe the W4 context model showing how it can represent a simple yet effective framework to enable flexible and general-purpose management of contextual information. In particular, we show the model suitability in describing user-centric situations, e.g., describing situations in terms of where a user is located and what he is doing. Then, we illustrate a set of algorithms to semantically enrich W4 represented data and to extract relevant information from it. In particular, starting from W4 data, such algorithms are able to identify the places that matter to the user and to describe them semantically. Overall, we show how the context-model and the algorithms allow to create an high-level, semantic and context-aware diary-based service. This service meaningfully collects and classifies the user whereabouts and the places that the user visited.
pervasive computing context-awareness w4 model knowledge engineering location-aware services localization algorithms