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Patel, S.V.
- Securing Datawarehouse Using Multidimensional Modeling with Virtual Private Database
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National Journal of System and Information Technology, Vol 3, No 1 (2010), Pagination: 1-9Abstract
Enterprise data warehouses are often very large systems and serve many user communities. Data warehouse play a key role in the business intelligence and crucial decision making. In addition to the normal data warehouse functionalities, they require flexible and powerful security features. It is expected that the security capabilities must seamlessly be incorporated in an environment which has stringent performance and scalability requirements.This paper advocates new concept of security design i.e. depicting security mechanisms at conceptual design stage itself using multidimensional modeling. VPD has been selected as the tool to be used in the implementation phase that satisfies the security mechanism depicted at the design stage.Keywords
Data Warehouse Security, Multidimensional Modeling, Conceptual Design, Vpd, Dimensional Level Security, Attribute Level Security, Row Level Security
References
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- Laila Niedrite , Maris Treimanis, Darja Solodovnikova, Liga Grundmane, University of Latvia, Latvia . Development of Data Warehouse Conceptual Models: Method Engineering Approach 2007
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- Rodolfo Villarroel ,Eduardo Fernández-Medina and Mario Piattini,Juan Trujillo, A UML 2.0/OCL Extension for Designing Secure Data Warehouses 2006
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- An Effective Approach Using Bi Techniques to Analyze Process Control Parameters during Steel Products Manufacturing
Abstract Views :474 |
PDF Views:2
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National Journal of System and Information Technology, Vol 4, No 1 (2011), Pagination: 52-61Abstract
Analysis is a process of understanding data more precisely. The process of analysis differs according to the data we are trying to analyze. Typical steel product manufacturing involves many processes running 247 in sequence comprising of different process units. These process units have sensors generating useful data for process control. These parameters have to be analyzed and monitored in real time to get a good quality product.
The conventional methods to analyze these data are inefficient. Hence, in this paper we present an approach for analysis that combines various process control and quality control parameters, uses online analytical processing (OLAP) system and provides flexibility to the user for necessary analysis.
Keywords
Data Warehouse, Online Analytical Processing, Statistical Process Control, Statistical Quality Control, CubeReferences
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- Kesheng Wang,Applying data mining to manufacturing: the nature and implications, Journal of Intelligent Manufacturing, 2007
- WANG Cai,TANG Hui-jia, School of Information Science and Technology,Southwest Jiaotong University,Chengdu 610031,China), Application research of manufacturing fault analysis base on warehouse and OLAP,2007
- Yang Guang,Zhang Lei and Ai Bo, Beijing University of Posts and Telecommunications,Data Warehouse and Online Analytical Processing, 2000
- Gary Fisher, Mineral Ridge, Ohio, Mark Clark, Mercer Force Company, Patent no. 54440478, 1995
- Shakar kumar choudhari, Sunil kumar, Vinit K Mathur, Application of Datamining technology at Tata Steel, 2004