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Graphical user interface for relational database
| Details |
Inventors: Shah, Arun; Novy, Robert F.; Ertl, Robert A.;
Assignee: Hyperion Solutions Corporation (Sunnyvale, CA)
Primary Examiner: Mizrahi; Diane D.
Assistant Examiner:
Attorney, Agent or Firm: Baker & McKenzie
Disclosed is a system, method, and apparatus for calculating metrics by using hierarchical level metadata to describe the various structures within the database. The hierarchical level metadata permit calculation of complex metrics by an analytical server which would otherwise be difficult or impossible. As a result of the way that the analytical server calculates the metrics, slicing and drilling are supported. Additionally, dimension and fact level security are also supported. |
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DETAILED DESCRIPTION Referring now to FIG.
1, there is illustrated a block diagram describing an exemplary computer architecture 100, configurable in accordance with an embodiment of the present invention.
The computer architecture 100 comprises a relational database management system (RDBMS) 105, a database or data warehouse 110, an interface 140, and an analytical server 120.
The database 110 is accessible by the analytical server engine 120.
The analytical server engine 120 accepts requests for metric calculations from clients 135, uses the metadata structures 145 to identify the necessary fact components and the best star schema for accessing them, generates and executes structured queries in a database query language, such as Structured Query Language (SQL), performs outer joins to conform query results, calculates the desired metrics, and returns them to the clients in a structured form such as multidimensional cubes.
The clients access the analytical server via an application programming interface (API) 140, through which metrics can be requested, possibly constrained on dimensional values.
The query and metric calculation results are transmitted through the interface as objects.
The client need not have knowledge of how the metric is calculated.
The database 110 includes a collection of base_fact tables 125a and dimension tables 125b organized in multiple star schemas 125.
Exemplary star schemas are described in Ralph Kimball, THE DATA WAREHOUSE TOOLKIT (John Wiley & Sons 1996), which is hereby incorporated by reference for all purposes.
Additionally, the database includes aggregate fact tables 130.
The aggregate_fact tables 130 contain values summarized from the base_fact tables 125a to certain specified levels of one or more dimensions.
An aggregate fact table 130 is more efficient and preferable to access than a base_fact table 125a, provided the level of detail of a given aggregate_fact table 130 is still sufficient for a given query.
Additionally, a set of metadata structures 145 describe the contents of, and relationships between, the various fact and dimension tables 125a, 125b
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