In total a d- dimensional data warehouse is associated with 3/4 d sub cuboids. In practice, they are normally pre-computed so as to improve the efficiency of user query. We describe cluster based implementation of an algorithm to compute this multidimensional model named data cube. Though a ...
First published on TECHNET on Feb 03, 2012 This is the eighth and last post in a series of posts detailing the capabilities of OLAP cubes in the Data...
First published on TECHNET on Feb 01, 2012 IntroductionService Manager 2012 data warehouse provides users a model-based approach to build OLAP cubes.
In Data Warehouse language, slicing and dicing is done with Dimension Attributes. Sometime a developer feels the need to provide everything to end users, whereas seasoned Business Intelligence Architects understand to provide only the attributes from the requirements. It is a hard path to follow an...
All of the existing (iceberg) cube computation algorithms assume that the data is stored in a single base table, however, in practice, a data warehouse is often organized in a schema of multiple tables, such as star schema and snowflake schema. In terms of both computation time and space,...
Many approaches have been proposed to pre-compute data cubes in order to efficiently respond to OLAP queries in data warehouses. However, few have proposed solutions integrating all of the possible outcomes, and it is this idea that leads the integration of hierarchical dimensions into these respon...
In recent years however, the rise of data warehouses, columnar storage, and cheaper, more powerful computers has led to a paradigm shift in the analytics world: the focus on optimizing for analytics speed has given way to prioritizing the usability of a dataset, and making sure that data is...
The work presented in this paper aims to build OLAP cubes from big data warehouses implemented by using the columnar NoSQL model. The use of NoSQL models is motivated by the inability of the relational model, usually used to implement data warehousing, to allow data scalability easily. Indeed...
During loading of a dimension, you may require to preserve existing data when new data comes in. For example, let's say that the warehouse records initially that a city has population 5000. Now new data comes in which indicate the city has a new population of 6000. Instead of simply over...
摘要: Data Warehouse (DW) and OLAP systems are effective solutions for the online analysis of large volumes of data structured as cubes. Usually organizations and enterprises require several cubes for their关键词:OLAP Data Warehouse Top-K queries ...