Data Warehouse and Data Virtualization Comparative Study

Currently, in the competitive world of business organizations is changing. This has led to the decision-making systems are changing, and as it is known that the decision-making systems rely entirely on Business Intelligence and this in turn depends on the accuracy of the data integration process. However, there are two main types of data integration, the first one is physical integration represented by Data Warehouse, while the other named by virtual integration represented by Data Virtualization. In this paper, we review both techniques (Data Warehouse and Data Virtualization) in addition to the listed usefulness and disadvantages of each, the main contribution of this paper is a comparison between (DW and DV) to help researchers in this area and the developers of the best decision-making through the process of building their applications.

[1]  Barry Devlin,et al.  Data Warehouse: From Architecture to Implementation , 1996 .

[2]  Richard D. Hackathorn,et al.  Using the Data Warehouse , 1994 .

[3]  Ralph Kimball,et al.  The Data Warehouse Lifecycle Toolkit , 2009 .

[4]  Veronika Stefanov,et al.  A UML Profile for Modeling Data Warehouse Usage , 2007, ER Workshops.

[5]  Alberto O. Mendelzon,et al.  Temporal Queries in OLAP , 2000, VLDB.

[6]  J. A. Nasir,et al.  Architecture for Virtualization in Data Warehouse , 2007 .

[7]  Cécile Favre,et al.  A user-driven data warehouse evolution approach for concurrent personalized analysis needs , 2008, Integr. Comput. Aided Eng..

[8]  Jigeesh Nasina,et al.  A Virtual Data Warehouse for Manufacturing Industry , 2010 .

[9]  Norshuhada Shiratuddin,et al.  Evaluation Framework for Business Process Evaluation Approaches , 2016 .

[10]  Detlef D. Nauck,et al.  Real Time Business Intelligence for the Adaptive Enterprise , 2006, The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (CEC/EEE'06).

[11]  Ayad H. Mousa Al-Badri,et al.  DEVELOPING STRATEGIC REPORTS FOR NATIONAL CO-OPERATIVE OF MALAYSIA (ANGKASA) USING DATA WAREHOUSE AND DECISION TREE MODEL , 2011 .

[12]  Peter Chamoni,et al.  Temporal Structures in Data Warehousing , 1999, DaWaK.

[13]  Olivera Marjanovic The Next Stage of Operational Business Intelligence: Creating New Challenges for Business Process Management , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[14]  Jayanthi Ranjan,et al.  Real time business intelligence in supply chain analytics , 2008, Inf. Manag. Comput. Secur..

[15]  Wagner Meira,et al.  Assessing Data Virtualization for Irregularly Replicated Large Datasets , 2006, Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID'06).

[16]  Lakshmi S. Iyer,et al.  Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing , 2002, Decis. Support Syst..

[17]  C. S. Ramanigopal,et al.  Business intelligence for infrastructure and construction industry , 2012 .

[18]  Rick F. van der Lans Deploying Data Virtualization in Business Intelligence Systems , 2012, miproBIS 2012.

[19]  Johann Eder,et al.  Changes of Dimension Data in Temporal Data Warehouses , 2001, DaWaK.

[20]  Rick van der Lans,et al.  Data Virtualization for Business Intelligence Systems: Revolutionizing Data Integration for Data Warehouses , 2012 .

[21]  Jigeesh Nasina,et al.  Creating a Virtual Data Warehouse for Manufacturing Industry , 2012 .

[22]  Paul T. Murphy,et al.  An Architecture for a Business and Information System , 1988, IBM Syst. J..

[23]  Ralph Kimball,et al.  The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing and Deploying Data Warehouses with CD Rom , 1998 .