Abstract
A typical cloud platform provides capability of scalability, elasticity and fault tolerance. Moreover, it is designed to deal with high volumes of data on nearly unlimited number of machines. On-Line Analytical Processing (OLAP), a kernel part of modern decision support systems, allows interactive analysis of multidimensional data of varied granularity. A combination of the Cloud Computing and OLAP technologies brings challenges in providing OLAP analysis services in distributed environments. This paper presents an overview of our on-going research on Elastic OLAP Cloud Platform. The design issues and implementation details are discussed, including research challenges, architecture, index and dynamic extension mechanisms, OLAP Modeling Markup Language, and related services. The experiment and performance results demonstrate the feasibility and effectiveness of the developed platform.