An overview of data warehousing and OLAP technology
ACM SIGMOD Record
CubiST: a new algorithm for improving the performance of ad-hoc OLAP queries
Proceedings of the 3rd ACM international workshop on Data warehousing and OLAP
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Cost-based query transformation in Oracle
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Partitioned optimization of complex queries
Information Systems
SCOPE: easy and efficient parallel processing of massive data sets
Proceedings of the VLDB Endowment
Oracle Essbase & Oracle OLAP
SAS 9.3 OLAP Server: User's Guide
SAS 9.3 OLAP Server: User's Guide
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The JovianDATA MDX engine is a data processing engine, designed specifically for managing multidimensional datasets spanning several terabytes. Implementing a terascale, native multidimensional database engine has required us to invent new ways of loading the data, partitioning the data in multi-dimensional space and an MDX (MultiDimensional eXpressions) query compiler capable of transforming MDX queries onto this native, multi-dimensional data model. The ever growing demand for analytics on huge amount of data needs to embrace distributed technologies such as cloud computing to efficiently fulfill the requirements. This paper provides an overview of the architecture of massively parallel, shared nothing implementation of a multi-dimensional database on the cloud environment. We highlight our innovations in 3 specific areas - dynamic cloud provisioning to build data cube over a massive dataset, techniques such as replication to help improve the overall performance and key isolation on dynamically provisioned nodes to improve performance. The query engine using these innovations exploits the ability of the cloud computing to provide on demand computing resources.