Data Warehousing and OLAP: MOLAP and ROLAP dr. Toon Calders t.calderstue.nl. Data cubes as a.dimensional OLAP MOLAP approach, but also a relational OLAP ROLAP solution. Relational OLAP ROLAP using SAS OLAP Server and Teradata.Describes the advantages and the disadvantages of the different OLAP technologies: MOALP, ROLAP, and HOLAP.ROLAP relational online analytical processing is an alternative ...

OLAP mining is a mechanism which integrates on-line analytical processing (OLAP) with data mining so that mining can be performed in diierent portions of databases or data warehouses and at diierent levels of abstraction at user's nger tips. With rapid developments of data warehouse and OLAP technologies in database industry, it is promising to develop OLAP mining mechanisms. With our years of ...

Both data mining and OLAP are powerful decision support tools. However, people use them separately for years: OLAP systems concentrate on the efficiency of building OLAP cubes, and no statistical / data mining algorithms have been applied; on the other hand, statistical analysis are traditionally developed for two-way relational databases, and have not been generalized to the multi-dimensional ...

On-Line Analytical Processing (OLAP) systems provide good performance and ease-of-use when retrieving summary information from very large amounts of data. However, the complex structures and relationships inherent in related non-summary data are not handled well by OLAP systems. In contrast, object database systems are built to handle such complexity, but do not support summary querying well. T...

OLAP applications are widely used in business applications. They are often (implicitly) defined on top of OLTP systems and extensively use aggregation and transformation functions. The main OLAP data structure is a multidimensional table with three kinds of attributes: so-called dimension attributes, implicit attributes given by aggregation functions and fact attributes. Domains of dimension at...

Data warehouse and OLAP are core aspects of business intelligence environments, since the former store integrated and time-variant data, while the latter enables multidimensional queries, visualization and analysis. The bitmap join index has been recognized as an efficient mechanism to speed up queries over data warehouses. However, existing OLAP tools does not use strictly this index to improv...

Data warehousing and On-Line Analytical Processing (OLAP) are two of the most signiicant new technologies in the business data processing arena. A data warehouse can be deened as a \very large" repository of historical data pertaining to an organization. OLAP refers to the technique of performing complex analysis over the information stored in a data warehouse. The complexity of queries require...

Recently, the case has been made for operational or real-time Business Intelligence (BI). As the traditional separation into OLTP database and OLAP data warehouse obviously incurs severe latency disadvantages for operational BI, hybrid OLTP&OLAP database systems are being developed. The advent of the first generation of such hybrid OLTP&OLAP database systems requires means to characterize their...

SAS/OLAP Server® Software is a powerful, flexible and scalable OLAP solution. It’s hybrid OLAP (HOLAP) architecture provides the flexibility to store data in whatever format is most appropriate based on the characteristics of the data and how the data will be used. This also means, however, that choices must be made as to what is the most appropriate data model for a successful OLAP application...