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Título: Sparse data estimation for knowledge processes
Autores: Lari, Kamran A.
Fecha: 2004
Publicador: McGill University - MCGILL
Fuente:
Tipo: Electronic Thesis or Dissertation
Tema: Engineering, Industrial.
Descripción: During recent years, industry has increasingly focused on knowledge processes. Similar to traditional or manufacturing processes, knowledge processes need to be managed and controlled in order to provide the expected results for which they were designed. During the last decade, the principals of process management have evolved, especially through work done in software engineering and workflow management.
Process monitoring is one of the major components for any process management system. There have been efforts to design process control and monitoring systems; however, no integrated system has yet been developed as a "generic intelligent system shell". In this dissertation, an architecture for an integrated process monitoring system (IPMS) is developed, whereby the end-to-end activities of a process can be automatically measured and evaluated. In order to achieve this goal, various components of the IPMS and the interrelationship among these components are designed.
Furthermore, a comprehensive study on the available methodologies and techniques revealed that sparse data estimation (SDE) is the key component of the IPMS which does not yet exist. Consequently, a series of algorithms and methodologies are developed as the basis for the sparse data estimation of knowledge based processes. Finally, a series of computer programs demonstrate the feasibility and functionality of the proposed approach when applied to a sample process. The sparse data estimation method is successful for not only knowledge based processes, but also for any process, and indeed for any set of activities that can be modeled as a network.
Idioma: en