Introduction: OLAP servers present business users with multidimensional data from data warehouses or data marts, without concerns regarding how or where the data are stored. However, the physical architecture and implementation of OLAP servers must consider data storage issues.
Implementations of a warehouse server for OLAP processing include the following:
Relational OLAP (ROLAP) servers: These are the intermediate servers that stand in between a relational back-end server and client front-end tools. They use a relational or extended-relational DBMS to store and manage warehouse data, and OLAP middleware to support missing pieces. ROLAP servers include optimization for each DBMS back end, implementation of aggregation navigation logic, and additional tools and services. ROLAP technology tends to have greater scalability than MOLAP technology. The DSS server of Micro strategy, for example, adopts the ROLAP approach.
Multidimensional OLAP (MOLAP) servers: These servers support multidimensional views of data through array-based multidimensional storage engines. They map multidimensional views directly to data cube array structures. The advantage of using a data cube is that it allows fast indexing to pre computed summarized data. Notice that with multidimensional data stores, the storage utilization may be low if the data set is sparse. In such cases, sparse matrix compression techniques should be explored (Chapter 4). Many MOLAP servers adopt a two-level storage representation to handle dense and sparse data sets: denser sub cubes are identified and stored as array structures, whereas sparse sub cubes employ compression technology for efficient storage utilization.
Hybrid OLAP (HOLAP) servers: The hybrid OLAP approach combines ROLAP and MOLAP technology, benefiting from the greater scalability of ROLAP and the faster computation of MOLAP. For example, a HOLAP server may allow large volumes of detail data to be stored in a relational database, while aggregations are kept in a separate MOLAP store. The Microsoft SQL Server 2000 supports a hybrid OLAP server.
Specialized SQL servers: To meet the growing demand of OLAP processing in relational databases, some database system vendors implement specialized SQL servers that provide advanced query language and query processing support for SQL queries over star and snowflake schemas in a read-only environment.
“How are data actually stored in ROLAP and MOLAP architectures?” Let’s first look at ROLAP. As its name implies, ROLAP uses relational tables to store data for on-line analytical processing. Recall that the fact table associated with a base cuboids is referred to as a base fact table. The base fact table stores data at the abstraction level indicated by the join keys in the schema for the given data cube. Aggregated data can also be stored in fact tables, referred to as summary fact tables. Some summary fact tables store both base fact table data and aggregated data, as in Example 3.10. Alternatively, separate summary fact tables can be used for each level of abstraction, to store only aggregated data.
MOLAP uses multidimensional array structures to store data for on-line analytical processing. Most data warehouse systems adopt client-server architecture. A relational data store always resides at the data warehouse/data mart server site. A multidimensional data store can reside at either the database server site or the client site.
