Commenced in January 2007
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Agile Methodology for Modeling and Design of Data Warehouses -AM4DW-

Authors: Nieto Bernal Wilson, Carmona Suarez Edgar


The organizations have structured and unstructured information in different formats, sources, and systems. Part of these come from ERP under OLTP processing that support the information system, however these organizations in OLAP processing level, presented some deficiencies, part of this problematic lies in that does not exist interesting into extract knowledge from their data sources, as also the absence of operational capabilities to tackle with these kind of projects.  Data Warehouse and its applications are considered as non-proprietary tools, which are of great interest to business intelligence, since they are repositories basis for creating models or patterns (behavior of customers, suppliers, products, social networks and genomics) and facilitate corporate decision making and research. The following paper present a structured methodology, simple, inspired from the agile development models as Scrum, XP and AUP. Also the models object relational, spatial data models, and the base line of data modeling under UML and Big data, from this way sought to deliver an agile methodology for the developing of data warehouses, simple and of easy application. The methodology naturally take into account the application of process for the respectively information analysis, visualization and data mining, particularly for patterns generation and derived models from the objects facts structured.

Keywords: Big Data, data warehouse, model data, object fact, object relational fact, process developed data warehouse

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[1] N. Schahovska, Data warehouse and Dataspace – information base of Decision Support System, CADSM’2011, 23-25, pp 170-173.
[2] V. Stefanov, B. List, Business Metadata for the Data warehouse Weaving Enterprise Goals and Multidimensional Models, 10th IEEE International Enterprise Distributed Object Computing Conference Workshops (EDOCW'06), pp 0-7695-2743-4/06.
[3] Boutkhoum, Hanine, Tikniouine, Agouti, Integration approach of multicriteria analysis to OLAP systems: Multidimensional model, ISBN 978-1-4799-0792-2, 20013, pp 1-4.
[4] H. Kuchibhotla, D. Dunn, D. Brown, Data Integration Issues in IT Organizations and a need to map different data formats to store them in relational databases, 41st Southeastern Symposium on System Theory University of Tennessee Space Institute Tullahoma, TN, USA, March 15-17, 2009, pp: 1-6.
[5] M. Mohajir, A Latrache, Unifying and incorporating functional and non functional requirements in Data warehouse conceptual design, 978-1-4673-2725-1/12 ©2012 IEEE, pp 49-57.
[6] A. Singh, Implementation Model for Access Control using Log Based Security, 2015 International Conference on Advances in Computer Engineering and Applications (ICACEA). IMS Engineering College, Ghaziabad, India, pp 290-293.
[7] A. Januszewski, T. Pankowski, Modeling Analytical Indicators Using Data warehouse Meta model, Proceedings of the 17th International Conference on Database and Expert Systems Applications (DEXA'06), pp 0-7695-2641.
[8] M. Mior Nasir, Enriching Hierarchies in Multidimensional Model of Data Warehouse using WORDNET, 3rd International Conference on Research and Innovation in Information Systems – 2013 (ICRIIS’13), pp 296-301.
[9] O. Boutkhoum, M. Hanine, A. Tikniouine, T. Agouti, Integration approach of multicriteria analysis to OLAP systems: Multidimensional model, 978-1-4799-0792-2/13/$31.00 ©2013 IEEE. pp without number.
[10] C. S. Jensen, A. Kligys, T. B. Pedersen, I. Timko, “Multidimensional data modeling for location-based services”, VLDB Journal, vol. 13(1), pp. 1–21, 2004.
[11] E. Malinowski and E. Zimányi, Advanced data warehouse design: from conventional to spatial and temporal applications, Springer, first edition, 2008.
[12] Z. Kouba, K. Matousek, and P. Miksovský, “On data warehouse and GIS integration, 11th International Conference on Database and Expert Systems Applications”, DEXA, pp. 604–613.
[13] A. C. Ferreira, M. L. Campos, and A. K. Tanaka, “An architecture for spatial and dimensional analysis integration”, 6th World Multiconference on Systemics, Cibernetics and Informatics (SCI), pp. 392–395.
[14] S. Shekhar, C. T. Lu, X. Tan, and S. Chawla, Map cube: a visualization tool for spatial data warehouses, chapter in: H. J. Miller, J. Han (eds.), Geographic data mining and knowledge discovery, Taylor and Francis, pp. 74 – 109.