INFORMATION CHANGE THE WORLD

International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.8, No.7, Jul. 2016

APESS - A Service-Oriented Data Mining Platform: Application for Medical Sciences

Full Text (PDF, 583KB), PP.36-42


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Author(s)

Mohammed Sabri, Sidi Ahmed Rahal

Index Terms

Knowledge Discovery;Services Oriented Architecture (SOA);Web Services;Data Warehouse; Data Mining;Rules Discovery;Medical sciences

Abstract

The domain medical and public health remains the principal preoccupation of all world population. It makes recourse at several means from various disciplines, including for instance epidemiology, to help clinicians in decision processes. This paper proposes an Assistance Platform for Epidemiological Searches and Surveillance (APESS) for service-oriented data mining in the field of epidemiology. The main aim of the present platform is to build a system that enables extracting predictive rules, flexible and scalable for aid in decision-making by trades' experts. Results showed that the current system provides prediction models of chronic diseases (epidemiological prediction rules), using classification algorithms.

Cite This Paper

Mohammed Sabri, Sidi Ahmed Rahal,"APESS - A Service-Oriented Data Mining Platform: Application for Medical Sciences", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.7, pp.36-42, 2016. DOI: 10.5815/ijitcs.2016.07.06

Reference

[1]N. Chen, N. C. Marques, and N. Bolloju, “A Web Service-based approach for data mining in distributed environments,” in Proceeding of the 1th International Workshop on Web Services: Modeling, Architecture and Infrastructure (WSMAI-2003), Angers, France, Apr. 2003, pp. 74-81.

[2]W. K Cheung, “Scalable and privacy preserving distributed data analysis over a service-oriented platform,” Chapter 7 in the book : Data Mining Techniques in Grid Computing Environments, W. Dubitzky, University of Ulster, UK, 2008, pp. 105-118.

[3]R. A. Ferreira, O. G. Dorgival, and W. Jr. Meira, “Anteater: A Service-Oriented Data Mining,” Chapter 11 in the book : Data Mining Techniques in Grid Computing Environments, W. Dubitzky, University of Ulster, UK, 2008, pp. 179-198.

[4]A. Kumar and M. Singh, “An Empirical Study on Testing of SOA based Services,” International Journal of Information Technology and Computer Science, vol.01, issue 07, pp.54-66, 2015.

[5]D. Birant, “Service-Oriented Data Mining,” Chapter 1 in the book: New Fundamental Technologies in Data Mining, K. Funatsu and K. Hasegawa, Published by InTech, Croatia, Jan. 2011, pp.3-18. 

[6]A. A Shaikh and O. F Rana, “FAEHIM: Federated Analysis Environment for Heterogeneous Intelligent Mining,” Chapter 6 in the book : Data Mining Techniques in Grid Computing Environments, W. Dubitzky, University of Ulster, UK, 2008, pp. 91-104.

[7]T. S. Sobh and M. Fakhry, “Evaluating Web Services Functionality and Performance,” International Journal of Information Technology and Computer Science, 2014, vol.05, Issue 03, pp. 18-27.

[8]Institut National de Santé Publique, "Transition épidémiologique et système de santé, “Enquête Nationale Santé,” Projet TAHINA (Contrat n° ICA3-CT-2002-10011), Ministère de la Santé, de la Population et de la Réforme Hospitalière, Algeria, Nov. 2007.

[9]A. A Shaikh, O. F Rana, and I. J. Taylor, “Web services composition for distributed data mining,” in Proceeding of the 34th International Conference on Parallel Processing Workshops (ICPP 2005 Workshops), Oslo, Norway, Jun. 2005, pp. 11-18.

[10]C. C. Chiu and M. H. Tsai, “A Dynamic Web Service based Data Mining Process System,” in Proceeding of the 5th International Conference on Computer and Information Technology (CIT 2005), Shanghai, China, 2005, pp.1033-1039.

[11]P. Chen, B. Wang, L. Xu, B. Wu, and G. Zhou, “The Design of Data Mining Metadata Web Service Architecture Based on JDM in Grid Environment,” in Proceeding of the 1st International Symposium on Pervasive Computing and Applications, Urumqi, China, 2006, pp. 684-689.

[12]L. Xu, Y. Wang, G. Geng, X. Zhao, and N. Du, “SDMA: A Service based Architecture for Data Mining Applications,” IEEE International Conference on Services Computing, Jul. 2008, pp. 473-474.

[13]D. Talia, P.Trunfioy, and O. Verta, “The Weka4WS framework for distributed data mining in service-oriented Grids,” Concurrency and Computation: Practice and Experience, vol. 20, no. 16, 2008, pp.1933-1951.

[14]C. B. C. Latha, P. Sujni, E. Kirubakaran, and S. Narayanan, “A Service Oriented Architecture for Weather Forecasting Using Data Mining,” The International Journal of Advanced Networking and Applications (IJANA), vol. 02, no. 02, 2010, pp. 608-613.

[15]M. Zorrilla and D. García-Saiz, “A service oriented architecture to provide data mining services for non-expert data miners,” Decision Support Systems, Elsevier, vol. 55, no. 1, Apr. 2013, pp. 399-411.

[16]R. R. Shelke, R. V. Dharaskar, and V. M. Thakare, “Data mining for mobile devices using web services,” in Proceeding of the International Conference on Industrial Automation And Computing (ICIAC), Lonara, Nagpur, 12th & 13th April 2014, Published in : International Journal of Engineering Research and Applications (IJERA), vol. 8, no. 2, Apr. 2014, pp. 7-9.

[17]Q-A Kester and W. Sam-Aggrey, “Open System Architecture Platform for Big Data: An Integrated Emergency Disaster Response System Architecture,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 5, Issue 4, April 2015.

[18]V. Patil and K. Kadam, “A Survey Paper on Implementing Service Oriented Architecture for Data Mining,” International Journal on Recent and Innovation Trends in Computing and Communication, vol.3, Issue. 7, July 2015, pp. 4815 – 4817.

[19]U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, “Advances in Knowledge Discovery and Data Mining,” American Association for Artificial Intelligence Menlo Park, 1996.

[20]L. Soibelman, M. Asce, and K. Hyunjoo, “Data Preparation Process for Construction Knowledge Generation through Knowledge Discovery in Databases,” Journal Of Computing In Civil Engineering, vol. 16, no. 1, Jan. 2002, pp. 39-48.

[21]F. Ravat, O.Teste, and G. Zurfluh, “Modélisation et extraction de données pour un entrepôt objet, ” Université Paul Sabatier (Toulouse III), IRIT (Institut de Recherche en informatique de Toulouse), équipe SIG, Toulouse, France 2001.

[22]N. Harbi, O. Boussaid, and F. Bentayeb, “ Propriétés d’un modèle conceptuel multidimensionnel pour les données complexes, ” in Proceeding of 8èmes Journées Francophones Extraction et Gestion des Connaissances, Sophia Antipolis, France, Jan. 2008, pp. 25-36.

[23]R. Kimball, “The data warehouse toolkit: practical techniques for building dimensional data warehouses,” John Wiley & Sons, Inc., New York, NY, USA, 1996.

[24]N. Selmoune, S. Boukhedouma, and Z. Alimazighi, “Conception d’un outil décisionnel pour la gestion de la relation client dans un site de e-commerce, ” in Proceeding of SETIT 3rd International Conference, Sousse, Tunisia, Mar. 2005.

[25]J. Han and M. Kamber, “Data Mining: Concepts and Techniques,” Morgan Kaufmann Publishers, Inc., San Francisco, CA, USA, 2001.

[26]J. R. Quinlan, “Learning efficient classification procedures and their applications to chess endgames,” Chapter 11 in the book : In Machine learning: An artificial intelligence approach, R. S. Michalski, J. G. Carbonell, & T. M. Mitchell, (Eds.), Tioga Publishing Co., Palo Acto, CA, USA, 1983, pp. 463-482.

[27]J. R. Quinlan, “C4.5: Programs for Machine Learning,” Morgan Kaufmann Publishers, Inc., San Mateo, CA, USA, 1993, Book Review in Machine Learning, Springer, vol. 16, no. 3, Sep. 1994, pp.235–240.