Social machines: a unified paradigm to describe, design and implement emerging social systems

Detalhes bibliográficos
Ano de defesa: 2014
Autor(a) principal: BURÉGIO, Vanilson André de Arruda
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Universidade Federal de Pernambuco
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: https://repositorio.ufpe.br/handle/123456789/12430
Resumo: The open, distributed approach of the Web and the relationship’s prevalence of applications and services are transforming both the way we develop software and how they operate and interact with each other. As a result, a novel breed of applications is emerging, and consequently new mental models are needed to deal with them. In this context, Social Machines appear as a promising model for developing software. However, it is a fresh topic, with concepts and definitions coming from different research fields, making a unified understanding of the concept a somewhat challenging endeavor. In this thesis we provide a more coherent conceptual basis for understanding Social Machines as a unified paradigm to describe, design and implement emerging social applications and services. To do that, we revisited the concept of relationship and extend the notion of Social Machines to establish a common abstraction model that is used for blending computational and social elements into software. Second, to describe social machines, this proposal presents an analysis guideline that addresses some issues related to the engineering exercise of existing systems. Third, we provide the Social Machine-oriented Architecture (SoMAr) - a hybrid style to design social machines through the combination of different principles from current software engineering practice. Finally, we discuss the experiences and lessons learned from applying the social machines paradigm in different contexts.