Method, article of manufacture, and apparatus for...

Details Method, article of manufacture, and apparatus for... Method, article of manufacture, and apparatus for...

1998-07-24

2001-05-01

Ho, Ruay Lian (Department: 2771)

Data processing: database and file management or data structures

Database design

Data structure types

C707S793000, C707S793000, C705S405000

Reexamination Certificate

active

06226647

ABSTRACT:

BACKGROUND OF THE INVENTION
A. Field of the Invention
The present invention is directed toward the field of information systems. More particularly, the present invention is directed toward the multi-dimensional organization, generation, maintenance, and viewing of two-pass values.
B. Description of Related Art
When analyzing information it is often desirable to scrutinize data that is derived from the information being analyzed. For example, when analyzing sales revenues contributed by a number of divisions in a corporation, it is useful to know what percentage of the corporation's total sales revenue is contributed by each division. Such a percentage value is referred to as a two-pass value, while the underlying sales revenue figures are referred to as one-pass values.
A one-pass value is a measure result that is obtained by performing some type of processing operation on a set of data records. A two-pass value is a measure result that is derived from processing a set of one-pass values. Examples of two-pass values include relative percentages, relative rankings, and running totals of one-pass values.
A raw data record contains values that are classified as being either a measure value or a dimension value. The dimension values characterize the measure values, and the measure values contain data to be either quantitatively or qualitatively analyzed.
A multi-dimensional view provides an environment in which a set of data records can be analyzed. Such analysis is made possible by converting the data records' measure values into one-pass value measure results and two-pass value measure results. The measure results are then displayed, so that they are characterized by the data records' dimension values.
For example, a company may have sold video cassette recorders (“VCR”), televisions (“TV”), and stereos in 1995 and 1996 in both an Eastern region and a Western region of the United States. The sales revenue measure values representing the VCR, TV, and stereo sales may be characterized by a number of different dimensions. One possible set of dimensions includes a region dimension, year dimension, and product dimension.
FIG.
1
(
a
) illustrates a multi-dimensional view
100
characterizing the company's sales with respect to the region, year, and product dimensions. The multi-dimensional view
100
is formed so that the cells
101
are filled with sales revenue measure results, which are obtained from a set of data records. Each axis
103
,
104
in the view
100
is divided into sections that represent a set of dimension values. Each section on the horizontal axis
104
corresponds to a unique pair of a year dimension value and a product dimension value. Each section on the vertical axis
103
corresponds to a unique region dimension value.
Each cell
101
in the view
100
contains a sales revenue measure result indicating the sales revenue of a product in a particular year in a particular region. For example, the upper lefthand cell in the view
100
contains a measure result indicating that there were $30,000 of VCR sales in the Eastern region of the United States in 1995. The sales revenue measure results appearing in the cells
101
in FIG.
1
(
a
) are one-pass values, because they have been directly derived by processing measure values in a set of data records.
FIG.
1
(
b
) illustrates an alternate multi-dimensional view
110
in which the cells
111
are filled with percentages, which are two-pass value measure results. The sections of the horizontal axis
114
and vertical axis
113
in the view
110
in FIG.
1
(
b
) are the same as for the view
100
in FIG.
1
(
a
). The only difference is that the measure results in the cells
111
contain the percentage of sales revenue that is contributed by each product for a combination of a region and a year.
For example, the upper lefthand cell in the FIG.
1
(
b
) view
110
indicates the percentage of Eastern sales revenue in 1995 that was attributable to VCR sales. This two-pass value measure result is 30%. The measure results in FIG.
1
(
b
) are two-pass values, because they are calculated based on the one-pass value measure results in the view shown in FIG.
1
(
b
).
The data records that are used as the ultimate basis for generating a multi-dimensional view are retrieved from a database or other source of records, such as a data file. Database management systems are employed to manage such databases. These systems provide for storing, accessing, and manipulating data records. Records can be extracted from a database management system by submitting a query to the system. In response to the query, the database management system searches the records in the database to identify and provide a set of records which correspond to the requirements set forth in the query.
One traditional method of preparing multi-dimensional views having two-pass value measure results is by using SQL statements. Through the use of SQL statements, a record management system submits queries to a database management system that will result in the receipt of data records containing two-pass value measure results. Such SQL statements request data records that include specified dimension values and measure values. These SQL statements also call for one-pass values to be derived from the requested data records and two-pass value measure results to be derived from the one-pass values. As a result, the two-pass value measure results for use in a multi-dimensional view are provided in response to the SQL statement query.
However, the computation time and memory resources required for calculating the two-pass value measure results using SQL statements is excessive. In order to account for many different possible multi-dimensional views, a great number of different two-pass value measure results are calculated. Such two-pass value measure results may include ranks and percentages for different multi-dimensional views that could possibly be formed from the retrieved data records. By calculating the two-pass value measure results for many possible views, some of the two-pass value measure results that are calculated will never be employed. Consequently, the time spent in calculating these measure results and the many resources used to store them were wasted.
Further, the use of SQL statements limits the number of possible multi-dimensional views of two-pass values that can be created from a set of retrieved data records. Since two-pass value measure results are all performed simultaneously with a query, no new two-pass value measure results can be obtained once the query in completed. For newly desired two-pass value measure results, an entirely new query must be performed, even if the information in the retrieved data records is sufficient for deriving the two-pass value measure results. This is undesirable, since the new query will cause delay and utilize additional memory resources. Such delay is unacceptable when operating in an on-line analytical processing (OLAP) environment.
Another traditional method for preparing multi-dimensional views having two-pass value measure results is to create a multi-dimensional record structure containing two-pass value measure results. However, this method suffers drawbacks that are similar to the SQL statement method described above.
FIG. 2
illustrates a traditional multi-dimensional record structure
90
characterizing the previously mentioned company's sales revenue percentages with respect to the region, year, and product dimensions. The record structure
90
is formed so that cells
91
1-12
in the structure
90
are filled with sales revenue percentage measure results. The x-axis
92
of the structure
90
is sectioned into regions that correspond to a set of product dimension values (VCRs, TVs, and stereos). The y-axis
93
of the structure
90
is sectioned into regions that correspond to a set of region dimension values (East and West). The z-axis
94
of the structure
90
is sectioned into regions that correspond to a set of year dimension values (1995 and 1996).
Each sales revenue per

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