| Texto completo | |
| Autor(es): |
Venero, Sheila Katherine
;
dos Reis, Julio Cesar
;
Montecchi, Leonardo
;
Fischer Rubira, Cecilia Mary
;
Assoc Comp Machinery
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | SAC '19: PROCEEDINGS OF THE 34TH ACM/SIGAPP SYMPOSIUM ON APPLIED COMPUTING; v. N/A, p. 10-pg., 2019-01-01. |
| Resumo | |
Knowledge-intensive processes (KiPs) cannot be fully specified at design time because not all information about the process is available prior to its execution. At runtime, new information emerges reflecting environment changes or unexpected outcomes. The structure of this kind of processes varies from case to case and it is defined step-by-step based on knowledge worker's decisions made after analyzing the current situation. These decisions rely on the knowledge worker's experience and available information. Current process management approaches still need to adequately address the complex characteristics of knowledge-intensive processes, such as their unpredictability, emergency, non-repeatability, and dynamism. This paper proposes a metamodel for representing KiPs aiming to help knowledge workers during the decision-making process. Domain and organizational knowledge are modeled by objectives and tactics. The metamodel supports the definition of objectives, metrics, tactics, goals and strategies at runtime according to a specific situation. Also, it includes concepts related to context and environment elements, business artifacts, roles and rules. The feasibility of our model was evaluated via a proof of concept in the medical domain. (AU) | |
| Processo FAPESP: | 17/02325-5 - EvOLoD: evolução de dados interconectados na Web Semântica |
| Beneficiário: | Julio Cesar dos Reis |
| Modalidade de apoio: | Auxílio à Pesquisa - Jovens Pesquisadores |
| Processo FAPESP: | 17/21773-9 - Desenvolvimento e Teste de Sistemas-de-Sistemas Resilientes: Uma Perspectiva de Arquitetura de Software |
| Beneficiário: | Cecília Mary Fischer Rubira |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |