Towards Personalized Web-Tasking: Task Simplification Challenges

Castañeda, Lorena; Müller, Hausi A.; Villegas, Norha M.

doi:10.1109/services.2013.61

Cited by 3 publications

(2 citation statements)

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“…Despite the existence of applications to automate and personalize web interactions, these are insufficient to guarantee the accomplishment of user goals when changes in relevant context cannot be fully anticipated at design time. Personalized Web-Tasking (PWT) is defined as the automation of repetitive web interactions that, together with the exploitation of personal context (e.g., information from personal profiles, social relationships, and historical web interactions), seeks to optimize user experiences by assisting people in the fulfilment of personal goals using internet technologies [2,4].…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive applications: on the development of personalized web-tasking systems

Castañeda

Villegas

Müller

2014

Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

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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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Section: Introductionmentioning

confidence: 99%

Self-adaptive applications: on the development of personalized web-tasking systems

Castañeda

Villegas

Müller

2014

Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

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“…At the core of smart web applications is a user-centric model-users and their concerns become the centre of all web interactions to improve the users' experience by providing personalization and automation for web interactions [1]. We define personalized web-tasking as the automation of repetitive tasks, while exploiting contextual informationabout the users and the environment [2]. Context representation is highly relevant in the realization of personalized web-tasking systems [3].…”

Section: Introductionmentioning

confidence: 99%

Personalized Web-Tasking Applications: An Online Grocery Shopping Prototype

Castañeda

Villegas

Müller

2014

2014 IEEE World Congress on Services

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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.

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