## 3 Discussion  
Designing interactive systems entails several problems. First, the corresponding entities have to be selected. Next, we need to identify the set of capabilities and characteristics that would define each entity taking the current domain into account. These capabilities will characterize them not only in the model but within the whole environment. Third, we need to choose the technical approach to develop these models, _i.e._, taxonomies, ontological, object oriented, _etc_. And finally, we have to develop such a system. Considering the reviewed literature we have identified several setbacks beyond the presented problems: dynamism, ignorance about user capabilities, uncertainty about collected information and domain dependency.  
Modeling with dynamism requires several requisites. First of all, a powerful sensor environment is needed. The system performance should be good enough to maintain the information validity and coherence. This is because temporal constraints, which determine how useful a piece of information is in the current context. Furthermore, information must be heterogeneous. Each entity should be independent, but it also will need to understand others. One of the main problems found in this review is that usually user's characteristics are considered as static. In Section 1.1 we introduce the concept of \"context disabilities\" which already address this point. Following this perspective we remark here the works by [PERSON] _et al_. [5], [PERSON] _et al_. [21] and [PERSON] _et al_. [36] in which user models are maintained and updated.  
Regarding the users, we deem that it is hard to model and understand their capabilities. Several approaches use capabilities information to perform adapted interaction with the user. Nonetheless, we think this approach is not very accurate. It would be desirable to have some physiological background to be able to model each capability of each user. Nevertheless, the work by [PERSON] _et al_. [32] makes this possible by making an abstraction about the capabilities and regarding more the user needs.  
Trusting the collected data from from users and context is also a significant problem. Usually context data come from sensor networks. Sensors can stop working or worse, which would be if they start giving non-sense data. [PERSON] _et al_. [21] avoid this issue by collecting explicit data from the user. On the other hand, [PERSON] and [PERSON] [57] deal with this problem by using an ontology and a reasoning engine which are capable of working with ambiguity and uncertain data.  
Finally, another problem that we emphasize in this paper is the domain dependency of the analyzed solutions. This dependency makes difficult to reuse and export these approaches in/to different domains. This is clearly shown in the presented Tables 1 and 2. Looking at the considered characteristics (the columns) we can see how different solutions deal with similar aspects. The problem comes when other solutions are not taken into account. Besides, modeling with ontologies have the advantage of reusing these ontologies, parts or classes that have been designed by others.  
Regarding at the modeling technique, we have remarked that [PERSON] and [PERSON] [40] demonstrate that using ontologies might be more appropriate for modeling context. This technique is similarly useful for users and devices. In fact, several ontologies model these and more entities: FOAF [97], UserModelOntology [98], CoBrA/SOUPA [99],.... But, of course, it is not the only or the best solution for modeling these entities. This depends on the system characteristics and on the designer. Here we remark on several alternatives: