Pierre Moukeli Mbindzoukou

Work place: CAMPUS IAI, B.P. 2263 Libreville, Gabon

E-mail: pierre.moukeli@gmail.com


Research Interests: Natural Language Processing, Operating Systems, Parallel Computing, Database Management System


Pierre Moukeli Mbindzoukou Born in 1958 in Gabon, he received a doctorate in computer science in 1992 from Claude Bernard University, Lyon – France. From 1988 to 1992, he was a researcher-student member of LIP (Laboratoire de l’Informatique du Parallélisme) at ENS-Lyon (Ecole Normale Supérieure de Lyon). Back to Gabon, he joined the African Institute of Computer Science (IAI), an inter-African engineer-degree school, where he is teacher - researcher. He is also a teacher at INPTIC (National Institute of Post and Information and Communication Technologies) at Libreville – Gabon. Since 2009, Pierre MOUKELI is also Adviser in Computer Science of the President of the Republic of Gabon. He is also a consultant and auditor with public and private Gabonese companies. His research topics include parallel computing, operating system, language theory and document processing.

Author Articles
A Stochastic Model for Simple Document Processing

By Pierre Moukeli Mbindzoukou Arsene Roland MOUKOUKOU David NACCACHE Nino TSKHOVREBASHVILI

DOI: https://doi.org/10.5815/ijitcs.2019.07.06, Pub. Date: 8 Jul. 2019

This work focuses on the stationary behavior of a simple document processing system. We mean by simple document, any document whose processing, at each stage of its progression in its graph of processing, is assured by a single person. Our simple document processing system derives from the general model described by MOUKELI and NEMBE. It is about an adaptation of the said general model to determine in terms of metrics and performance, its behavior in the particular case of simple document processing. By way of illustration, data relating to a station of a central administration of a ministry, observed over six (6) years, were presented. The need to study this specific case comes from the fact that the processing of simple documents is based on a hierarchical organization and the use of priority queues. As in the general model proposed by MOUKELI and NEMBE, our model has a static component and a dynamic component. The static component is a tree that represents the hierarchical organization of the processing stations. The dynamic component consists of a Markov process and a network of priority queues which model all waiting lines at each processing unit. Key performance indicators were defined and studied point by point and on average. As well as issues specific to the hierarchical model associated with priority queues have been analyzed and solutions proposed; it is mainly infinite loops.

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A Stochastic Model for Document Processing Systems

By Pierre Moukeli Mbindzoukou Jocelyn Nembe

DOI: https://doi.org/10.5815/ijieeb.2016.05.07, Pub. Date: 8 Sep. 2016

This work is focused on the stationary behavior of a document processing system. This problem can be handled using workflow models; knowing that the techniques used in workflow modeling heavily rely on constrained Petri nets. When using a document processing system, one wishes to know how the system behaves when a new document enters in order to give precise support to the manager’s decision. This requires a good analysis of the system’s performances. But according to many authors, stochastic models, specifically waiting lines should be used instead of Petri nets at a strategic level in order to lead such analysis. The need to study a new model comes from the fact that we wish to provide tools for a decision maker to lead accurate performance analysis in a document processing system. In this paper, amodel for document management systems in an organization is studied. The model has a static and a dynamic component. The static one is a graph which represents transitions between processing units. The dynamic component is composed of a Markov processes and a network of queues which model the set of waiting-lines at each processing unit. Key performance indicators are defined and studied point-wise and on the average. Formulas are given for some example models.

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