We present an approach to integrating extemporary annotations on topics of interest to a user with information on the context in which the annotation is being taken. Context is here defined in terms of the set of surrounding devices (Bluetooth Low Energy enabled smartphones, WiFi hotspots) and the current calendar event. Contextify is a context-aware Android application which detects the current context and organizes the notes taken within context, allowing a form of context-based retrieval. Thus, the detected context represents location, other people present (referring to their BLE equipped smartphones as user proxies), and events. Users can easily retrieve notes when they return to the context where they created them. A context similarity algorithm derived from the Jaro-Winkler string similarity algorithm is used to compare contexts. Each note is tagged by the user and the system suggests the most appropriate tag, among the already used ones, at annotation creation time: the suggestion is based on the similarity of the current context with the contexts associated with previously tagged notes.
Capturing and using context in a mobile annotation application / Bottoni, P.; Di Tommaso, F.; Panizzi, E.. - (2019), pp. 1-4. (Intervento presentato al convegno 18th International Conference on Mobile and Ubiquitous Multimedia, MUM 2019 tenutosi a Italian National Research Council (CNR), ita) [10.1145/3365610.3368418].
Capturing and using context in a mobile annotation application
Bottoni P.;Panizzi E.
2019
Abstract
We present an approach to integrating extemporary annotations on topics of interest to a user with information on the context in which the annotation is being taken. Context is here defined in terms of the set of surrounding devices (Bluetooth Low Energy enabled smartphones, WiFi hotspots) and the current calendar event. Contextify is a context-aware Android application which detects the current context and organizes the notes taken within context, allowing a form of context-based retrieval. Thus, the detected context represents location, other people present (referring to their BLE equipped smartphones as user proxies), and events. Users can easily retrieve notes when they return to the context where they created them. A context similarity algorithm derived from the Jaro-Winkler string similarity algorithm is used to compare contexts. Each note is tagged by the user and the system suggests the most appropriate tag, among the already used ones, at annotation creation time: the suggestion is based on the similarity of the current context with the contexts associated with previously tagged notes.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.