TAILIEUCHUNG - Database Modeling & Design Fourth Edition- P37

Database Modeling & Design Fourth Edition- P37: Database technology has evolved rapidly in the three decades since the rise and eventual dominance of relational database systems. While many specialized database systems (object-oriented, spatial, multimedia, etc.) have found substantial user communities in the science and engineering fields, relational systems remain the dominant database technology for business enterprises. | Online Analytical Processing OLAP 167 ing them in step with the fact tables as new data arrives. When a user requests summary data the OLAP system figures out which AST can be used for a quick response to the given query. OLAP systems are a good solution when there is a need for ad hoc exploration of summary information based on large amounts of data residing in a data warehouse. OLAP systems automatically select maintain and use the ASTs. Thus an OLAP system effectively does some of the design work automatically. This section covers some of the issues that arise in building an OLAP engine and some of the possible solutions. If you use an OLAP system the vendor delivers the OLAP engine to you. The issues and solutions discussed here are not items that you need to resolve. Our goal here is to remove some of the mystery about what an OLAP system is and how it works. The Exponential Explosion of Views Materialized views aggregated from a fact table can be uniquely identified by the aggregation level for each dimension. Given a hierarchy along a dimension let 0 represent no aggregation 1 represent the first level of aggregation and so on. For example if the Invoice Date dimension has a hierarchy consisting of date id month quarter year and all . complete aggregation then date id is level 0 month is level 1 quarter is level 2 year is level 3 and all is level 4. If a dimension does not explicitly have a hierarchy then level 0 is no aggregation and level 1 is all. The scales so defined along each dimension define a coordinate system for uniquely identifying each view in a product graph. Figure illustrates a product graph in two dimensions. Product graphs are a generalization of the hypercube lattice structure introduced by Harinarayan Rajaraman and Ullman 1996 where dimensions may have associated hierarchies. The top node labeled 0 0 in Figure represents the fact table. Each node represents a view with aggregation levels as indicated by the .

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