ication) includes identifying contributive data sources for rewriting the query as well as filtering irrelevant data sources according to the user profile. The result of this step is a set of relevant data sources that can contribute to the query rewriting. The goal of the third step (relevant sources combination) is to produce query rewritings by combin- ing sources selected in the previous phase. In this step, knowledge from the profile is used to prune irrelevant combinations. Finally, the fourth step (final enrichment) expands the candidate rewritings with profile predicates. This step is quite simple because the concerned profile predicates have been identified during the previous stages of the reformulation process. This approach has been implemented and evaluated on a significant benchmark [31]. Integrating virtual relations to the user query during query expansion is a dif- ficult task similar to the Steiner Tree Problem [29]. Due to its complexity, this task is solved using the Minimum Cost Path Heuristic [45]. Relevant data sources are identi- fied using the first part of the MiniCon algorithm [37] with an additional pruning rule. Relevant sources are then combined using an adaptation of the Apriori algo- rithm [9] which allows pruning irrelevant combination as soon as possible. It is important to mention that the query reformulation service can be partially used to perform only query rewriting without personalization or to perform only query enrichment according to a user profile. 10.3.3 PAM Deployment The PAM can be used to build new personalized applications or to provide person- alization features to existing applications (legacy applications). Even if the PAM is application independent, its deployment depends on the application needs and the architecture. We have identified three use cases of PAM usage: mediation- improvement PAM, application-improvement PAM, and service-providing PAM. This section presents the deployment and the usage of the PAM in each of these architectures. 10.3.3.1 PAM-M: Intr