Abstract With the development of databases, online transaction processing (OLTP) can no longer meet needs end users for database query and analysis, simple large databases by SQL not requirements user analysis. Therefore, analytical (OLAP) is proposed. concept. On one hand, we explained basic knowledge OLAP, including OLAP multidimensional data concept, structure, characteristics, etc. other es...

<span>Decision makers in the educational field always seek new technologies and tools, which provide solid, fast answers that can support decision-making process. They need a platform utilize students’ academic data turn them into knowledge to make right strategic decisions. In this paper, roadmap for implementing driven decision system (DSS) is presented based on an mart. The independent...

The management of enterprise-wide information requires that metadata should be shared and globally accessible by all the heterogeneous products found in today’s information technology environment. OLAP metadata includes various information about objects and the relationships among them in an OLAP system. OLAP tools are provided by different vendors and run on different hardware and software pla...

In contrast to queries for on-line transaction processing (OLTP) systems that typically access only a small portion of a database, OLAP queries may need to aggregate large portions of a database which often leads to performance issues. In this paper we introduce CR-OLAP, a scalable Cloud based Real-time OLAP system based on a new distributed index structure for OLAP, the distributed PDCR tree. ...

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...