Nos principales perspectives de recherche sont guid´ees par l’importance capitale du contexte d’ex´ecution dans les applications multim´edia futures. Parmi les ´el´ements du contexte, le rˆole central de l’utilisateur a ´et´e soulign´e dans nos travaux. La description des caract´eristiques de l’utilisateur utilise classiquement des m´eta-donn´ees pour exprimer syntaxiquement les profils, les pr´ef´erences ou tout autre param`etre de personnalisation. Au-del`a d’une repr´esentation statique, le probl`eme de la g´en´eration automatique de telles m´eta-donn´ees est ouvert. Cette g´en´eration est d’autant plus difficile que certaines m´eta-donn´ees sont subjectives. Dans notre travail par exemple, la variable d´ecisionnelle indiquant le niveau d’int´erˆet de l’utilisateur est par nature subjective et seulement partiellement observable. Inf´erer des valeurs pour des m´eta-donn´ees repr´esentant l’int´erˆet de l’utilisateur ou son profil est donc d´elicat.
Malgr´e ces difficult´es, nos approches d’adaptation en contexte partiellement observable nous ont per- mis de manipuler de telles variables cach´ees. Grˆace `a la d´efinition des r´ecompenses appropri´ees `a chaque probl`eme, nous avons pu substituer les valeurs des variables cach´ees par des descripteurs (implicites) form´es par des s´equences d’observations. Par exemple, une s´equence d’interactions sugg`ere un niveau implicite d’int´erˆet ou l’appartenance implicite `a un profil.
En ce sens, nous esp´erons pouvoir utiliser notre approche d’adaptation pour proposer une m´ethodologie g´en´erale de profiling implicite. Cette d´emarche est possible d`es lors que :
1. l’optimisation d’un cumul de r´ecompense peut ˆetre d´efini et guider la conception d’un service adaptatif,
2. consid´erer diff´erents profils est susceptible d’am´eliorer les performances du service.
Parmi les applications `a fort potentiel, il serait donc possible d’adapter un service de recherche d’in- formation en profilant implicitement les utilisateurs qui ´emettent des requˆetes et consultent les r´esultats retourn´es par le moteur de recherche. La fonction de recherche du moteur pourrait ainsi ˆetre adapt´ee au fil des interactions. Les syst`emes de recommandation pourraient ´egalement b´en´eficier de cette approche. Cette derni`ere possibilit´e est d’autant plus pertinente que des syst`emes de recommandation `a base de PDM ont d´ej`a ´et´e propos´es mais, `a notre connaissance, aucun n’utilise des variables cach´ees pour tenter de caract´eriser plus finement les utilisateurs. A ces perspectives g´en´erales communes aux deux premi`eres parties de cette th`ese, se rajoutent les perspectives plus pr´ecises que nous avons ´enum´er´ees `a la fin des chapitres 2 et 3.
Pour la derni`ere partie, nos futurs travaux se concentreront sur des exp´erimentations avec des flux et des sc´enarii multim´edia plus complexes. En outre, nous envisageons d’int´egrer dans notre service MModChannel des canaux associ´es `a diff´erentes contraintes de fiabilit´e partielle. Ainsi, une premi`ere extension vers une fiabilit´e partielle concerne le protocole FIFO. A titre d’exemple, on peut consid´erer satisfaisant de d´elivrer un certain pourcentage de messages sur une fenˆetre glissante (par exemple 80% des messages sur une fenˆetre de dix messages), tout en respectant l’ordre FIFO. La deuxi`eme extension pourrait porter sur la causalit´e. Nous ´etudions une relaxation possible de la causalit´e. Sur une fenˆetre glissante, si un sous-ensemble de messages est d´elivr´e causalement, on peut parler de causalit´e partielle. Il serait int´eressant de positionner cette approche par rapport aux diff´erentes variantes de la Δ-causalit´e. Par ailleurs, une ´etude plus fouill´ee des param`etres permettant de quantifier la tol´erance perceptuelle li´ee `a la causalit´e, serait n´ecessaire. Il est ´evident que la prise en compte fine de ces param`etres est fortement d´ependante de l’application (jeux vid´eo r´epartis, travail coop´eratif, etc.). Enfin, notre API MModChannel pourrait aussi ˆetre utilis´ee pour assurer la coordination d’une pr´esentation stream´ee plus riche (par exemple d´ecrite en SMIL), o`u certains flux peuvent ˆetre d´elivr´es en respectant plusieurs ordres partiels.
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