Towards a Scalable, Performance-Oriented OLAP Storage Engine

  • Todd Eavis
  • Ahmad Taleb
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7239)


Over the past generation, data warehousing and OLAP applications have become the cornerstone of contemporary decision support environments. Typically, OLAP servers are implemented on top of either proprietary array-based storage engines (MOLAP) or as extensions to conventional relational DBMSs (ROLAP). While MOLAP systems do indeed provide impressive performance on common analytics queries, they tend to have limited scalability. Conversely, ROLAP’s table oriented model scales quite nicely, but offers mediocre performance at best relative to the MOLAP systems. In this paper, we describe a storage and indexing framework that aims to provide both MOLAP like performance and ROLAP like scalability by essentially combining some of the best features of both. Based upon a combination of R-trees and bitmap indexes, the storage engine has been integrated with a robust OLAP query engine prototype that is able to fully exploit the efficiency of the proposed storage model. Experimental results demonstrate that not only does the framework improve upon more naive approaches, but that it does indeed offer the potential to optimize both query performance and scalability.


Dimension Table Data Cube Query Performance Fact Table View Manager 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Todd Eavis
    • 1
  • Ahmad Taleb
    • 2
  1. 1.Concordia UniversityMontrealCanada
  2. 2.College of Computer Science and Information SystemsNajran UniversitySaudi Arabia

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