Petri Net Workflow Modeling for Digital Publishing- Measuring Quantitative Dependability Attributes

Digital Publishing consists of different job treatment stages involved in the correct set up of each job to be printed, but this process has been seriously hampered by a number of difficulties including the management of faults and errors of incoming jobs. This work describes workflow modeling using Workflownets and Generalized Stochastic Petri Nets (GSPN) for the Digital Publishing business process and how the dependability attributes are measured in a quantitative form. In our novel approach, these are measured from the workflow model itself, improving the analysis tools necessary to achieve a good workflow process definition. Applying these measure concepts to the Digital Publishing pre-press process provides a workflow management based on its trustworthiness. We also propose a methodology for measuring dependability from a workflow process definition. The main contribution of this work is a methodology to obtain values of dependability attributes in a quantitative form from the workflow model instead from a combinatorial model adapting some methodologies and strategies for combinatorial model construction to the creation of the workflow process definition. The book is addressed to professionals in Computer Science and Computer Engineering, as well as, persons related to the Digital Publishing Industry.

Teacher-Instructor, School of Engineering, Turabo University, PR, USA. Studies of M.S. in Computer Eng., University of Puerto Rico (2006) and Research Assistant for PDC-Lab, UPRM (2004/06). B.E. in Electrical Eng., Javeriana University, Colombia (2002). National Award for the Technology Innovation Winner, Colombia (2002).