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Ítem Exploiting social context in personalized web-tasking applications(IBM Corp., 2014-11-03) Villegas Machado, Norha MilenaPersonalized Web-Tasking (PWT) systems automate ordinary and repetitive web interactions while exploiting personal context to deliver personalized features. Among the personal context of a user, social context is all information obtained from the relationships with other users, which is relevant to the user's personalized web-tasks. Current approaches exploit the information of social media, or the explicit input of the user, and use it as is. In addition to this, PWT systems also benefit by inferring social relationships through reasoning over such information and other sources of context. For example, a calendar application might record events the user shares with other people, or the sensors on mobile devices can be used to identify others nearby. This information can be exploited to improve the execution of PWT applications including its personalization and context-adaptive capabilities.Ítem Self-adaptive applications: on the development of personalized web-tasking systems(ACM Press; Association for Computing Machinery, 2014-06-02) Müller, Hausi A.Personalized Web-Tasking (PWT) proposes the automation of user-centric and repetitive web interactions to assist users in the fulfilment of personal goals using internet systems. In PWT, both personal goals and internet systems are affected by unpredictable changes in user preferences, situations, system infrastructures and environments. Therefore, self-adaptation enhanced with dynamic context monitoring is required to guarantee the effectiveness of PWT systems that, despite context uncertainty, must guarantee the accomplishment of personal goals and deliver pleasant user experiences. This position paper describes our approach to the development of PWT systems, which relies on selfadaptation and its enabling technologies. In particular, it presents our runtime modelling approach that is comprised of our PWT Ontology and Goal-oriented Context-sensitive web-tasking (GCT) models, and the way we exploit previous SEAMS contributions developed in our research group, the DYNAMICO reference model and the SmarterContext Monitoring Infrastructure and Reasoning Engine. The main goal of this paper is to demonstrate how the most crucial challenges in the engineering of PWT systems can be addressed by implementing them as self-adaptive software.