Analytical server including metrics engine

• US 7,031,953 B2
• Filed: 12/08/2003
• Issued: 04/18/2006
• Est. Priority Date: 04/17/2000
• Status: Expired due to Term

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Hyperion Solutions Corporation (Oracle Corporation)
• Inventors: Novy, Robert F., Shah, Arun
• Primary Examiner(s): AMSBURY, WAYNE P
• Application Number:
  US10/731,773
• Publication Number:
  US 20040122813A1
• Time in Patent Office: 862 Days
• Field of Search: 707/2
• US Class Current: 707/769
• CPC Class Codes: G06F 16/2454 Optimisation of common expressions
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 21/6218 to a system of files or objects, e.g. local or distributed file system or database
• CPC Class Codes: G06F 21/6227 where protection concerns the structure of data, e.g. records, types, queries
• CPC Class Codes: Y10S 707/954 Relational
• CPC Class Codes: Y10S 707/956 Hierarchical
• CPC Class Codes: Y10S 707/957 Multidimensional
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

Disclosed is a method for determining database queries to be sent by an analytical server to a Relational Database Monitoring System (RDBMS) by using hierarchical level metadata to describe the various structures within the database.

Claims

1. A method of executing a metric query for a relational database, the relational database having fact tables, dimension tables, and metadata describing the relationships between the fact and dimension tables, wherein the fact tables include measures which can be additive or non-additive, and wherein the metadata includes a designation specifying by dimension which measures are additive and which measures are non-additive, the method comprising:

receiving the metric query from a client;

determining which hierarchical levels of the fact and dimension tables are available in the relational database for responding to the metric query;

determining at least one database query according to the available hierarchical levels of the fact and dimension tables;

retrieving data corresponding to the at least one database query from the relational database; and

responding to the metric query based on the data retrieved, whereby the response is tailored to the most efficient hierarchical level necessary depending on whether the measures of the requested metric are additive.

2. A method according to claim 1, wherein the metric query calls for a metric broken down across a requested dimension and also calls for a roll-up of that metric.

3. A method according to claim 2, wherein the determining of the at least one database query takes into account whether the requested metric is additive specifically across the requested dimension.

4. A method according to claim 1, wherein

the metric query is received by an analytical server which is positioned between the client and a Relational Database Management System (RDBMS);

the relational database is located on the RDBMS; and

the method is operable on the analytic server to provide an interface between the client and the relational database.

5. A method according to claim 4, wherein the analytical server receives from the RDBMS at least a portion of the metadata information including information about whether certain measures are additive and information about the logical hierarchy of the fact and dimension tables.

6. A method according to claim 5, wherein the available hierarchical levels of the fact and dimension tables are determined based on the metric query received from the client and based on the received metadata information from the RDBMS regarding the logical hierarchy of the fact and dimension tables and whether the measures of the requested metric are additive.

7. A method of executing a metric query for a relational database, the relational database having fact tables, dimension tables, and metadata describing the relationships between the fact and dimension tables, the method comprising:

receiving the metric query from a client;

determining which hierarchical levels of the fact and dimension tables are available in the relational database for responding to the metric query, wherein at least one measure within the fact and dimension tables are insufficiently fine in a requested dimension in order to respond to the metric query;

determining at least one metric by which the at least one measure which was unavailable at the requested fineness can be approximated by the using the measure along the requested dimension at a higher hierarchical level than the one needed to directly respond to the metric query; and

sending a database query to the relational database based on the determining of available hierarchical levels of the fact and data tables and upon at least one higher hierarchical level for the needed measure along the dimension which was unavailable at the needed hierarchical level.

8. A method according to claim 7, wherein the determining of which hierarchical levels of the fact and dimension tables are available in the relational database is based on a logical hierarchy of the fact and dimension tables and based on the metric query received from the client.

9. A method according to claim 8, wherein the at least one metric by which the at least one measure which was unavailable at the requested fineness is determined according to the available hierarchical levels of the fact and dimension tables.

10. A method according to claim 7, wherein

the metric query is received by an analytical server which is positioned between the client and a Relational Database Management System (RDBMS);

the relational database is located on the RDBMS; and

the method is operable on the analytic server to provide an interface between the client and the relational database.

11. A method according to claim 10, wherein the analytical server receives, from the RDBMS, information about whether certain measures are additive and information about the logical hierarchy of the fact and dimension tables.

12. The method of claim 7, wherein the measure which was unavailable at the requested fineness was a time measure.

13. The method of claim 12, wherein a requested hierarchical level along the time dimension was for a measure broken down by month.

14. The method of claim 13, wherein to approximate the measure on a monthly basis, a measure broken down by quarter is divided by three and assigned to the requested months.

15. The method of claim 13, wherein to approximate the measure on a monthly basis, the measure is repeated at the requested months.

16. The method of claim 15, wherein the measure was an average.