ABSTRACT
Yazid Benazzouz, Philippe Beaune, Fano Ramaparany, and Olivier
Boissier
Abstract Servi es need to be adapted to users and auto-ongured for optimizing their fun tioning. Adaptation requires servi es to be aware of
their ontexts. However, making ontext-aware servi es osts many eorts
from designers and a deli ate setting. We propose to automate this pro ess
by identifying relevant ontexts for servi es and then adapting these servi es
a ordingly. To a omplish our purpose, we explore a ontext data-driven
approa h. This approa h refers to the pro ess of olle ting and storing data
from a wide range of sour es. Data are gathered in onformity with a ontext
data model, stored in databases and then ontext re ognition te hniques are
applied to identify relevant ontexts. Our proposal diers from the existing
approa hes for ontext-aware appli ations, where ontext models and appli-
ations are losely related and ignore how ontext is derived from sour es
and interpreted. Our approa h is supported by an overall ar hite ture for
servi e adaptation and intends to automati ally adapt servi es without any
prior knowledge about ontext models.
