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ROLAP (relational online analytical processing) is an alternative to the MOLAP (Multidimensional OLAP) technology. While both ROLAP and MOLAP analytic tools are designed to allow analysis of data through the use of a multidimensional data model, ROLAP differs significantly in that it does not require the pre-computation and storage of information. Instead, ROLAP tools access the data in a relational database and generate SQL queries to calculate information at the appropriate level when an end user requests it. With ROLAP, it is possible to create additional database tables (''summary tables'' or ''aggregations'') which summarize the data at any desired combination of dimensions. While ROLAP uses a relational database source, generally the database must be carefully designed for ROLAP use. A database which was designed for OLTP will not function well as a ROLAP database. Therefore, ROLAP still involves creating an additional copy of the data. However, since it is a database, a variety of technologies can be used to populate the database. == ROLAP vs. MOLAP〔 〕 == The discussion of the advantages and disadvantages of ROLAP below, focus on those things that are true of the most widely used ROLAP and MOLAP tools available today. In some cases there will be tools which are exceptions to any generalization made. === Advantages of ROLAP === * ROLAP is considered to be more scalable in handling large data volumes, especially models with dimensions with very high cardinality (i.e., millions of members). * With a variety of data loading tools available, and the ability to fine-tune the ETL code to the particular data model, load times are generally much shorter than with the automated MOLAP loads. * The data are stored in a standard relational database and can be accessed by any SQL reporting tool (the tool does not have to be an OLAP tool). * ROLAP tools are better at handling ''non-aggregatable facts'' (e.g., textual descriptions). MOLAP tools tend to suffer from slow performance when querying these elements. * By decoupling the data storage from the multi-dimensional model, it is possible to successfully model data that would not otherwise fit into a strict dimensional model. * The ROLAP approach can leverage database authorization controls such as row-level security, whereby the query results are filtered depending on preset criteria applied, for example, to a given user or group of users (SQL WHERE clause). 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「ROLAP」の詳細全文を読む スポンサード リンク
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