@inproceedings{nabelsi_detecting_2015,

title = {Detecting constraints in supply chain reengineering projects: Case study of data and process integration in a hospital pharmacy},

author = {V. Nabelsi and S. Gagnon},

editor = {Zaremba M. Sasiadek J. Dolgui A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953887968&doi=10.1016%2fj.ifacol.2015.06.066&partnerID=40&md5=ce9be2cbe2fdcfc4872793c13f4228a2},

doi = {10.1016/j.ifacol.2015.06.066},

isbn = {24058963 (ISSN)},

year  = {2015},

date = {2015-01-01},

booktitle = {IFAC-PapersOnLine},

volume = {28},

pages = {106–111},

abstract = {This paper discusses how messy data may be a hidden failure factor that Business Process Reengineering (BPR) projects typically cannot detect during the planning phase. Our case study deals with Supply Chain Management (SCM) within two major urban hospitals, involving $2 million in minimum stocks for drug inventory. Our project addresses the feasibility of the hospital's data warehousing integration, especially at the stage of Extract, Transform, and Load (ETL). We conclude with a proposed system architecture audit and verification method that may serve to guide reengineering project planning and execution. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},

note = {Issue: 3 

Journal Abbreviation: IFAC-PapersOnLine},

keywords = {Administrative data processing, Artificial intelligence, Business Process, Business process management, Business process management (BPM), Business process re-engineering, Case-studies, Data integration, Data mining, Data models, Data structures, Data warehouses, Decision support system, Decision support system (dss), Decision support systems, Enterprise resource management, Extract transform and load, Extract Transform and Load (ETL), Hospitals, Information management, Integration, Process management, Project case, Re-engineering projects, Reengineering, Supply chain management, Supply Chain Management (SCM), Supply chain managements (SCM), System architectures, Verification method, Verification of information system},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{allili_finite_2007,

title = {Finite generalized Gaussian mixture modeling and applications to image and video foreground segmentation},

author = {M. S. Allili and N. Bouguila and D. Ziou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548671710&doi=10.1109%2fCRV.2007.33&partnerID=40&md5=be89ffce30db18d0716df9eba2a197a2},

doi = {10.1109/CRV.2007.33},

isbn = {0-7695-2786-8 978-0-7695-2786-4},

year  = {2007},

date = {2007-01-01},

booktitle = {Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007},

pages = {183–190},

address = {Montreal, QC},

abstract = {In this paper, we propose a finite mixture model of generalized Gaussian distributions (GDD) for robust segmentation and data modeling in the presence of noise and outliers. The model has more flexibility to adapt the shape of data and less sensibility for over-fitting the number of classes than the Gaussian mixture. In a first part of the present work, we propose a derivation of the Maximum-Likelihood estimation of the parameters of the new mixture model and we propose an information-theory based approach for the selection of the number of classes. In a second part, we propose some applications relating to image, motion and foreground segmentation to measure the performance of the new model in image data modeling with comparison to the Gaussian mixture. © 2007 IEEE.},

keywords = {Data structures, Finite mixture models, Foreground segmentation, Image segmentation, Information theory, Mathematical models, Maximum likelihood estimation, Mixture of General Gaussions (MoGG)},

pubstate = {published},

tppubtype = {inproceedings}

}