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 paper describes our approach to the development of PWT systems, which relies on self-adaptation 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 DYNAM-ICO 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.

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Despite the increasing use of the web to support human activities, most web interactions required to accomplish personal goals are performed manually by users. Even though users can easily transform a goal into multiple web interactions, the manual governance of these interactions diminishes the user experience. Personalized web-tasking seeks to improve the user experience by automating personal web tasks. This automation is driven by user needs, matters of concerns, and personal context. An important concern in personalized web-tasking is task simplification, the process of decomposing a personal web task into simpler tasks that can readily be composed into bigger tasks. This position paper characterizes a set of task simplification challenges intended as starting points for advancing the field of personalized web-tasking.

Nowadays, users utilize web applications to perform everyday tasks in order to achieve personal goals. Personalized Web-Tasking (PWT) is the automation of such web interactions while exploiting personal context to enrich users experience. However, web-tasking is affected by unpredictable context behaviour-environment, user, and infrastructureand situational changes. Given that current web systems are challenged to respond effectively to such changes, we proposed to design PWT applications as self-adaptive software systems that exploit personal context to deliver user-centric functionalities. This paper presents our first approach implementing PWT applications using a grocery shopping web-tasking scenario. Our prototype PWT system transforms web-tasking knowledge information (i.e., user's web interactions) into RDF graphs (i.e., runtime models that contain the user's web-tasking). We conclude our paper with a discussion about our results and implementation challenges.