Securing OLAP Cubes
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Online Analytical Processing (OLAP) has become an increasingly important and prevalent component of enterprise Decision Support Systems. OLAP is associated with a data model known as a Cube, a multi-dimensional representation that allows for the extraction and intuitive visualization of broad patterns and trends that would otherwise not be obvious to the user. One must note, however, that not all of the collected data should be universally accessible. Specifically, DW/OLAP systems almost always house confidential and sensitive data that must, by definition, be restricted to authorized users. I...
Online Analytical Processing (OLAP) has become an increasingly important and prevalent component of enterprise Decision Support Systems. OLAP is associated with a data model known as a Cube, a multi-dimensional representation that allows for the extraction and intuitive visualization of broad patterns and trends that would otherwise not be obvious to the user. One must note, however, that not all of the collected data should be universally accessible. Specifically, DW/OLAP systems almost always house confidential and sensitive data that must, by definition, be restricted to authorized users. In this book, we address this problem and provide a comprehensive end-to-end framework for OLAP security that is flexible, intuitive, and powerful. In short, the framework allows administrators to associate security policies with an intuitive conceptual model that maps directly to the model that users see. Restrictions then can be propagated transparently from users to all the hierarchical data. Finally, an automatic form of inference control is provided that is fast enough in practice to not affect query time.
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