Patent Publication Number: US-8126871-B2

Title: Systems and computer program products to identify related data in a multidimensional database

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of and claims the benefit of U.S. Pat. No. 7,472,127 B2 “SYSTEMS, METHODS, AND COMPUTER PROGRAM PRODUCTS TO IDENTIFY RELATED DATA IN A MULTIDIMENSIONAL DATABASE”, having U.S. application Ser. No. 10/325,299, filed Dec. 18, 2002, the entire contents of which is incorporated herein by reference. 
     In co-pending U.S. application Ser. No. 09/565,132 entitled “Navigating an Index to Access a Subject Multi-Dimensional Database,” by William Earl Malloy et. al, assigned to the assignee of the present invention, and incorporated herein in its entirety by this reference, there is described a method, apparatus, and article of manufacture for using an index to access a subject multidimensional database. Although not limited thereto, the present invention employs such a method in one of its preferred embodiments. U.S. application Ser. No. 09/565,132 issued as U.S. Pat. No. 7,269,786 on Sep. 11, 2007. 
     In co-pending U.S. application Ser. No. 09/564,344 entitled “Using an Index to Access A Subject Multi-Dimensional Database,” by William Earl Malloy et. al, assigned to the assignee of the present invention, and incorporated herein in its entirety by this reference, there is described a method, apparatus, and article of manufacture for using an index to access a subject multidimensional database. Although not limited thereto, the present invention employs such a method in one of its preferred embodiments. U.S. application Ser. No. 09/564,344 issued as U.S. Pat. No. 6,915,289 on Jul. 5, 2005, and a Statutory Disclaimer was filed for U.S. Pat. No. 6,915,289 on Jan. 10, 2007. U.S. Pat. No. 7,529,727, having U.S. application Ser. No. 09/747,515, entitled “Using an Index to Access A Subject Multi-Dimensional Database,” by William Earl Malloy et. al, with U.S. Publication Number 2001/0054034, is a continuation of U.S. application Ser. No. 09/564,344, and is incorporated herein in its entirety by this reference. 
     In co-pending U.S. patent application Ser. No. 09/998,955, “Systems, Methods, and Computer Program Products to Interpret, Explain, and Manipulate Exceptions in Multidimensional Data,” by Kelkar et al., assigned to the assignee of the present invention, there is described a method, apparatus, and article of manufacture for interpreting, explaining, and manipulating exceptions in multidimensional data on a computer system. Although not limited thereto, the present invention employs such a method in one of its preferred embodiments. U.S. application Ser. No. 09/998,955 issued as U.S. Pat. No. 6,654,764 on Nov. 25, 2003, and a Statutory Disclaimer was filed for U.S. Pat. No. 6,654,764 on Jan. 10, 2007. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention is directed to the field of computer-based multidimensional data modeling. It is more particularly directed to identifying data that is related to selected data in a multidimensional database on a computer system. 
     2. Description of the Background Art 
     On-Line Analytical Processing (OLAP) is a computing technique for summarizing, consolidating, viewing, analyzing, applying formulae to, and synthesizing data according to multiple dimensions. OLAP software enables users, such as analysts, managers, and executives, to gain insight into performance of an enterprise through rapid access to a wide variety of data “views” or “dimensions” that are organized to reflect the multidimensional nature of the enterprise performance data. An increasingly popular data model for OLAP applications is the multidimensional database (MDDB), which is also known as the “data cube.” OLAP data cubes are often used by a data analyst for interactive exploration of performance data. New opportunities associated with the enterprise may be discovered by identifying relationships and associations in the data. 
     OLAP functionality is characterized by dynamic multidimensional analysis of data supporting end user analytical and navigational activities including: calculation and modeling applied across dimensions through hierarchies or across members, trend analysis over sequential time periods, slicing subsets for on-screen viewing of the multidimensional data, drill-down to deeper levels of consolidation of the multidimensional data, reach-through to underlying detail data, and rotation to new dimensional comparisons in the viewing area associated with the multidimensional data. It is frequently difficult to efficiently analyze multidimensional data due to the lack of referential information about the association of the data to other neighboring and possibly related multidimensional data. 
     A multidimensional OLAP system typically has multiple dimensions and may have members within each dimension. A member may be considered a name of a category used in multidimensional analysis. That is, a member may be a label associated with an edge that edge being a dimension in a multidimensional data cube. For example, “March” could be a member that identifies information that was stored relating to the month of March. Such a system that supports a multidimensional data cube is often very large, and it may be difficult to identify where the most interesting features are in a vast pool of data. More particularly it is often difficult and time consuming to identify and analyze the most interesting features when the relationship and association between the data in a multidimensional data cube is unclear. 
     In order to facilitate access of information in a data cube, an index that may be represented in an index data cube and that references data in the data cube may be generated. The index may be used to access selected information in a data cube more efficiently than access techniques that do not employ an index. Given an index that is used to access and select particular multidimensional data, it would be useful to present the context in which the selected multidimensional data is located as a representation of neighboring or associated multidimensional data. 
     From the foregoing it will be apparent that there is still a need to improve OLAP data analysis by determining the relationship between selected multidimensional data and neighboring or associated multidimensional data on a computer system. More particularly, existing systems have not been able to adequately and efficiently determine the relationship between neighboring or associated data that may be configured in a database and that is associated with selected multidimensional data. 
     SUMMARY OF THE INVENTION 
     An embodiment of the present invention relates to systems, methods, and computer products that identify data that are related to selected data in a database on a computer system. The present invention is related to the field of computer-based multidimensional data modeling often used by data analysts. 
     The present invention assists the data analyst by efficiently identifying and more clearly presenting data that is related to or associated with selected multidimensional data such as selected data that is discussed with reference to U.S. patent application Ser. No. 09/565,132, “Navigating an Index to Access a Subject Multi-Dimensional Database.” Existing systems have not been able to efficiently and adequately identify data that is related to, or associated with, selected data in a multidimensional database. 
     The preferred embodiment of the present invention operates efficiently with an index that includes location and magnitude information about features in a subject multidimensional database. More particularly, selected data has been extracted from the subject multidimensional database and the preferred embodiment of the present invention uses a Related Data Identifier Module to access information in the index that is used to identify data that is related to and associated with the selected data. The overwhelming amount of data in a multidimensional database that may be viewed by a user, such as a data analyst, is efficiently and advantageously reduced, by the preferred embodiment of the present invention, to the selected and related data. 
     The preferred embodiment of the present invention operates on features of selected data that may be presented in a two-dimensional view or multi-dimensional view, and enhances the view with associated data that may be uniquely formatted. The associated data is related to the selected data along the dimensions of the view that are used to present the selected data. Further, the preferred embodiment of the present invention may identify the associated data as sibling data of the selected data. The sibling data is at the same level of aggregation in the subject multidimensional database as the selected data. The preferred embodiment of the present invention novelly accesses the selected data by using index data that is not necessarily at the same hierarchy level and the index data is not necessarily represented as a multidimensional database. 
     An embodiment of the present invention is achieved by systems, methods, and articles of manufacture that use an index that identifies data that is related to selected data in a multidimensional database on a computer system. The method of the preferred embodiment of the present invention comprises: (a) obtaining selected data, typically from a subject multidimensional database by the use of index data; (b) creating a view of the selected data; (c) creating a query that accesses the index data to obtain related index data; and (d) obtaining a view of the identified related data via the use of the related index data. Further the method may comprise: (e) creating a view of the related data; (f) formatting the view of the related data and the selected data for presentation to a user; and (g) highlighting attributes of the related data and the selected data in the view based on highlighting criteria. One embodiment of the present invention may also suppress information about the selected data in the subject database that is not interesting, such as empty data points. This enables the user to view related data without the inclusion of irrelevant data. 
     Other aspects and advantages of the present invention will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, illustrating by way of example the principles of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the following detailed description and in the several figures of the drawings, like elements are identified with like reference numerals. 
         FIG. 1  is a block diagram that illustrates the present invention; 
         FIG. 2A  is a block diagram of a multidimensional database, an OLAP data cube, that is suitably configured for operation with the present invention; 
         FIG. 2B  is a block diagram that illustrates the conceptual structure of a subject multidimensional database; 
         FIG. 2C  is a block diagram that illustrates the Related Data Identifier Module; 
         FIG. 3A  is a block diagram that illustrates the operation of the Related Data Identifier Module; 
         FIG. 3B  is a block diagram that illustrates the conceptual structure of an index multidimensional database; 
         FIG. 3C  is a block diagram that illustrates a query used to determine related data; 
         FIG. 4  is a flow diagram that illustrates the method of the present invention; and 
         FIG. 5  is a block diagram of a computer system suitably configured for employment of the present invention. 
     
    
    
     DESCRIPTION OF THE INVENTION 
     As shown in the drawings and for purposes of illustration, the preferred embodiment of the invention novelly identifies data that is related to and associated with data that has been selected from a multidimensional database. The overwhelming amount of data in a multidimensional database that may be viewed by a user, such as a data analyst, is reduced to the selected data that is presented along with multidimensional data that is related to and associated with selected data. According to the present invention, irrelevant data is filtered out and not presented to the user thereby providing a simplified view of the data for analysis. Existing systems have not been able to efficiently and adequately provide contextual information about selected data by identifying data that is related to and associated with selected data in a multidimensional database. 
     As shown in  FIG. 1  and in element  100 , the preferred embodiment of the present invention may operate in a client-server computer system configuration. It will be appreciated that other computer system environments may be used to practice the present invention and the described computer system environment should not be considered limiting. Therefore, a client computer system  104  may communicate with a server computer system  102  during the operation of the present invention. The related Data Identifier Module  120  operates in either the client  104  or the server  102  to perform the preferred embodiment of the present invention. For example, information may be communicated to either the server  102  or the client  104  via the user interface  117 . Through such communication, data selection criteria  132  and data report highlight criteria  130  may be established and may subsequently be used by the Related Data Identifier Module  120  to manipulate data  108 , such as multidimensional data  110 . The user interface  117  may communicate with the preferred embodiment of the present invention, either via batch input  119  or user input  118 . 
     Further, an OLAP data cube  106  may be configured in the memory  558  of either the client  104  or the server  102 . Alternatively, the OLAP data cube  106  may be configured in computer storage such as a disk  122 . Typically, the OLAP data cube  106  is configured in computer storage of a disk  122  associated with the server  102 , and the client  104  typically accesses portions of the multidimensional data  110 . The terms “OLAP data cube,” “data cube,” and “multidimensional database” will be used interchangeably herein. Element  558  is described with reference to  FIG. 5 . 
     The operation of the present invention uses a subject data cube  126  and an index data cube  124 . The subject data cube  126  is the multidimensional database  110  that is the subject of analysis by the user. The index data cube  124  is used to access selected data  304  in the subject data cube  126 . The index data cube  124  and the subject data cube  126  may be located in the memory  558  of a computer system  500  or on another computer media, such as the disk  122 . The preferred embodiment of the present invention novelly uses a data cube  106  to represent the index data cube  124  so that a multidimensional query may be used to determine the data  108  that is related to and associated with the information in the subject data cube  126 . Element  304  is described with reference to  FIG. 3 , and element  500  is described with reference to  FIG. 5 . 
       FIG. 2  includes  FIG. 2A ,  FIG. 2B , and  FIG. 2C . As shown in  FIG. 2A , an OLAP data cube  106  is suitably configured for operation with the present invention. Therefore, by means of explanation, an example of the operation of the present invention is described. The view  212  that may be represented as a row or column may be included in an OLAP data cube  106 , and is organized to reflect the multidimensional nature of the enterprise performance data  108 . In the present invention views  212 , particularly two-dimensional views  212  that are used to view selected data  304  in context, are generated by selecting pairs of dimensions  213  from the subject data cube  126  and slicing the subject data cube  126  along those dimensions  213  so that the data point from the subject data cube  126  is included in the slice. For example, in the present example the three dimensions  213  are included in planes that are sliced by either a row or a column and create the following views  212 : view i*j, as shown in element  232 , view i*k, as shown in element  230 , and view j*k, as shown in element  234 . Elements  106 ,  108 , and  126  are described with reference to  FIG. 1 , and element  304  is described with reference to  FIG. 3 . 
     Further, a dimension is a set or collection of related categorical elements, as defined by an analytic model. For example, all time periods in a fiscal calendar (including defined groups of periods such as weeks, months, quarters, and years) might form a Time Dimension  208 . In the same way all product identifiers (including groups of products) might form a Product Dimension  202 . Herein the Market Dimension  216  may be used to identify all product markets, such as East  256 , West  258 , and Central  260 . 
     A multidimensional data cube  106  typically includes data  108  in a hierarchical structure that may be represented as aggregated levels. In the present example, the actual name of the Time dimension  208  is Year  240 . The Year dimension  240  may be decomposed into levels. In the present example the Time dimension  208  includes a quarter level with: Qtr  1 , as shown in element  242 , Qtr  2 , as shown in element  244 , Qtr  3 , as shown in element  246 , and Qtr  4 , as shown in element  248  that are siblings with respect to the hierarchical structure represented. The Product dimension  202  may be decomposed into information about the following products: Soda  250 , Milk  252 , and Wine  254 . 
       FIG. 2B  is a block diagram that illustrates the conceptual structure of a subject multidimensional database  126 . Therefore, by means of example the subject multidimensional database  126  includes information about the Year  240 , Products  202 , and Market  216 . The information may be organized in a hierarchical fashion. Therefore the information about a Year  240  may include information about quarters such as: Qtr  1  as shown in element  242 , Qtr  2  as shown in element  244 , Qtr  3  as shown in element  246 , and Qtr  4  as shown in element  248 . Information at the same hierarchy level may also be presented for Product data  202  and Market data  216 . The Product data  202  may include information about: Soda  250 , Milk  252 , Wine  254 , and Beer  255 . The Market information  216  may include information about: the East  256  market, the West  258  market, and the Central  260  market. The subject database  126  often includes a Measures Dimension  290  that enumerates the quantities that are stored in the multidimensional database  110 , such as Profit, Income, Expense, and Inventory. In the present example, the Measures Dimension  290  has a single member, Sales  292 . 
     As shown in  FIG. 2C , the Related Data Identifier Module  120  includes elements used in the preferred embodiment of the present invention. The Related Data Identifier Module  120  is typically program code that may be embodied as a computer program  542  (as shown in  FIG. 5 ), and by means of example an embodiment of the Related Data Identifier Module  120  is described in U.S. patent application Ser. No. 09/565,132, “Navigating an Index to Access a Subject Multi-Dimensional Database.” The Index Module  270  is used by an embodiment of the present invention to determine an index that is used to access the subject data cube  126 . The preferred embodiment of the present invention creates the index data cube  124  that is used to access the subject data cube  126 . Elements  124  and  126  are described with reference to  FIG. 1 . 
     The Dimension Identifier Module  272  is used to identify selected data  304  that is represented as dimensions  213  for a preferred projection of multidimensional data  110 , such as is described in U.S. patent application Ser. No. 09/998,955, “Systems, Methods, and Computer Program Products To Interpret, Explain, and Manipulate Exceptions in Multidimensional Data.” The related Data Capture Module  274  is used to access the index data cube  124  and identify related data  306  that is associated with the selected data  304  by use of the related index data  310 . The Query Generator  276  is used to generate a query that is executed against the index data cube  124  to determine the related index data  310 . The Report Generator  278  is used to generate a report of the selected data  304  and the related data  306 . Such a report is shown in Table 6 herein. Further, the Highlight Generator  280  is used to highlight certain attributes of the selected data  304  and related data  306  that meet highlight criteria  130 . Elements  110  and  130  are described with reference to  FIG. 1 , element  213  is described with reference to  FIG. 2A , and elements  304 ,  306 , and  310  are described with reference to  FIG. 3 . 
       FIG. 3  includes  FIG. 3A ,  FIG. 3B , and  FIG. 3C .  FIG. 3A , as shown in element  300 , illustrates the novel operation of the Related Data Identifier Module  120 . The Related Data Identifier Module  120  obtains selected data  304  from a subject data cube  126 . The subject data cube  126  is created from multidimensional data  110  that is typically stored on a disk  122 . Element  212  is described with reference to  FIG. 2 . 
     The Related Data Identifier Module  120  novelly operates according to the present invention by performing multidimensional queries on the index data cube  124  in order to identify related index data  310 . Since the index data cube  124  is typically sparse, identifying related index data  310  by the use of multidimensional queries is an efficient way of identifying, in a single operation, all selected data  304  in a given view  212  of the subject data cube  126  that is referenced by the index data  124 . Therefore, and in the preferred embodiment of the present invention, the index data cube  124  is represented as a multidimensional cube  106  so that the multidimensional queries directed to the index data cube  124  may be performed efficiently. Further, the members of identified selected data  304  determine the predicate or predicates of the multidimensional queries. Therefore, the Related Data Identifier Module  120  may novelly identify related data  306  that is associated with the selected data  304  by use of multidimensional queries directed to the related index data  310 . Further the selected data  304  and the related data  306  may be presented to the user in a view  212 , such as a selected data view  314  and a related data view  316 . 
       FIG. 3B , as shown in element  124 , illustrates the conceptual structure of the index data cube  124 . In one embodiment of the present invention, a system is based on the product marketed under the trademark Hyperion® Essbase® OLAP software. The present invention utilizes a number of components from the Essbase® OLAP system, including the outline illustrated herein. However, since the present invention comprises new elements that enable identification of related data  306  that is associated with selected data  304  that has been selected from a multidimensional database  110 , those skilled in the art will recognize that the present invention may be practiced without components based on Hyperion® Essbase® OLAP software. For example, there are a number of multidimensional systems that use Multidimensional Expressions (MDX) as their query language. Element  110  is described with reference to  FIG. 1 , and elements  304  and  306  are described with reference to  FIG. 3A . 
     In the present example, the multidimensional structure of the index data cube  124  may be represented as an outline, as shown in element  320 . The Measures Dimension  290  has only a single member, Deviation Magnitude  332 . The index data cube  124  identifies cells that fall within particular thresholds that are applied to the data in the subject database  126 . Further, as shown in Table 1, the present example of the index data cube  124  includes information about the values of Sales  292  having Deviation Magnitudes  332  at or above eight. Therefore in the present example the Products  202  shown are: Soda as shown in element  250 , and Beer as shown in element  255 . The Deviation Magnitudes  332  shown in Table 1 represent the deviations from the mean for the identified Sales  292  figures and serve to standardize the information that is presented. Element  126  is described with reference to  FIG. 1 , and element  272  is described with reference to  FIG. 2 . 
     It will be appreciated by those skilled in the art that there are many methods of calculating a deviation magnitude  332  associated with data  108  in the multidimensional data cube  110 . Such a method is described in U.S. Pat. No. 6,094,651, “Discovery-Driven Exploration of OLAP Data Cubes.” Other index data cubes  124  in the present example that are associated with the Market data  214  are East as shown in element  256  and Central as shown in element  260 . Element  108  is described with reference to  FIG. 1 . 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Index Data: 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                   
                 Deviation 
               
               
                   
                 Measure 
                 Year 
                 Product 
                 Market 
                 Magnitude 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Sales 
                 Qtr 1 
                 Soda 
                 East 
                 11 
               
               
                   
                 Sales 
                 Qtr 1 
                 Soda 
                 Central 
                 9 
               
               
                   
                 Sales 
                 Qtr 4 
                 Soda 
                 Central 
                 9 
               
               
                   
                 Sales 
                 Qtr 1 
                 Beer 
                 Central 
                 9 
               
               
                   
                 Sales 
                 Qtr 4 
                 Beer 
                 East 
                 8 
               
               
                   
                   
               
            
           
         
       
     
     Given the index data cube  124  illustrated in  FIG. 3B , further information about a particular row may be obtained by operation of the present invention. For example, the feature shown in Table 2, is an example of related index data  310  that is discussed with reference to  FIG. 3A . Therefore in this example, the related index data  310  of Table 2 may drive the operation of the present invention. 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 Related Index Data: 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                   
                 Deviation 
               
               
                   
                 Measure 
                 Year 
                 Product 
                 Market 
                 Magnitude 
               
               
                   
                   
               
               
                   
                 Sales 
                 Qtr 1 
                 Soda 
                 East 
                 11 
               
               
                   
                   
               
            
           
         
       
     
     As shown in Table 3, subject data  304  is used to present related data  306  that is located by use of information in the related index  310 . Therefore, as shown in Table 3, Sales  292  data about Soda  250  in all Markets  216  and for all quarters in the Year  240  is described. 
     
       
         
           
               
             
               
                 TABLE 3 
               
             
            
               
                   
               
               
                 Related Data - Sales Figures: 
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 Soda 
                   
                   
               
               
                   
                 Sales 
                 East 
                 West 
                 Central 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 Qtr 1 
                 −9.32 
                 1.20 
                 20.6 
               
               
                   
                 Qtr 2 
                 1.34 
                 1.32 
                 1.56 
               
               
                   
                 Qtr 3 
                 1.60 
                 1.34 
                 1.21 
               
               
                   
                 Qtr 4 
                 1.81 
                 1.10 
                 30.4 
               
               
                   
                   
               
            
           
         
       
     
     Continuing with the current example, the information associated with three entries in Table 3 falls within a threshold, such as highlight criteria  130 , that may be set by the user. It may be understood that the highlight criteria  130  enables the user or an administrator to constrain the points that are highlighted for any purpose, such as security or specialized analysis. Therefore, since the data shown in Table 4 falls within a threshold it represents related data  306  with Sales  292  figures of interest. The fact that the data for East  256  in Qtr  1   242  falls within the identified threshold is not a surprise because the view of data in Table 3 was driven by the selection of the index entry shown in Table 3. However, with very large subject data cubes  126  and complex methods for determining which features to include in the index data cube  124 , it may not be apparent that the Sales  292  figures in the last two rows of Table 4 also fall within the identified threshold specified in the creation of the index data cube  124 . Element  130  is described with reference to  FIG. 1 . 
     
       
         
           
               
             
               
                 TABLE 4 
               
               
                   
               
               
                 Significant Sales Figures: 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Sales 
                 Qtr 1 
                 Soda 
                 East 
                 −9.32 
               
               
                   
                 Sales 
                 Qtr 1 
                 Soda 
                 Central 
                 20.6 
               
               
                   
                 Sales 
                 Qtr 4 
                 Soda 
                 Central 
                 30.4 
               
               
                   
                   
               
            
           
         
       
     
     Now, as shown in  FIG. 3C  and element  340 , a query of the index data cube  124  is performed according to the preferred embodiment of the present invention. The Essbase® report script language query is shown in  FIG. 3C . However, it will be appreciated by those skilled in the art that the present invention may be practiced with components based on multidimensional software other than Hyperion® Essbase® OLAP software. The example query fixes on non-expanded members of the related data  306 , namely Soda  250  and Sales  392 , as shown in lines  2  and  3 . The other two dimensions  213 , Year  240  and Market  216  are chosen to generate the view  212  and as shown in element  344  the report script declares these two dimensions  213  as row labels in line  4 . The present example shows that a dimension  213  is novelly used by the present invention to create a representation of selected data  304  and related data  306 . For each selected dimension  213 , a representative set of siblings (members of the same level) of the member that identifies the selected data  304  within that dimension  213 , are used to retrieve related data  306 . By means of further explanation, the related data  306  are retrieved by accessing combinations of members that are at the same level of aggregation, in the hierarchies of the Subject Data Cube  126  as the Selected Data  304 . For example sibling members include Qtr  4  as shown in element  248 , and Central as shown in element  260 , respectively. In the present example, the sibling members are obtained by use of the directives shown in elements  346  and  348 , and are further shown with reference to Table 6. Since the index data cube  124  contains only indexed features of the subject database  126  the result is highly sparse and the query operation is highly efficient. The query  340  result of the search of the index data cube  124 , the related index data  310 , is shown in Table 5. Notice that the data points that are not interesting, such as empty data points, have been suppressed in the reporting of the indexed features of the subject database  126 . Elements  110 ,  124 , and  126  are described with reference to  FIG. 1 ; elements  202 ,  204 ,  212 ,  213 ,  216 ,  234 ,  240 ,  248  and  250  are described with reference to  FIG. 2 ; elements  304 ,  306 , and  310  are described with reference to  FIG. 3A ; and element  340  is described with reference to  FIG. 3C . 
     
       
         
           
               
             
               
                 TABLE 5 
               
               
                   
               
               
                 Query Result: 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Soda 
                 Sales 
                 Qtr 1 
                 East 
                 11 
               
               
                   
                 Soda 
                 Sales 
                 Qtr 1 
                 Central 
                 9 
               
               
                   
                 Soda 
                 Sales 
                 Qtr 4 
                 Central 
                 9 
               
               
                   
                   
               
            
           
         
       
     
     The view  212  of the subject database  126  is now augmented by the preferred embodiment of the present invention by highlighting the features identified by the query. The resulting view  212  of the subject database  126  shows the selected data  304  in context with other multidimensional data  110  at the same level of aggregation. Further, related data  306  are identified by use of the related index data  310  and in the present example the related data  306  in the related data view  316  are shown in bold format. The selected data point  304  is shown within a selected data view  314  in both bold and underlined format. Therefore, the selected data  304  and the related data  306  may be formatted and presented to the user in one highlighted view  212 , as shown in Table 6. While the present example has included two-dimensional views  212 , the operation of the present invention is not limited to two-dimensional views  212  and may operate with multi-dimensional views  212 . Elements  314  and  316  are described with reference to  FIG. 3A   
     
       
         
           
               
             
               
                 TABLE 6 
               
             
            
               
                   
               
               
                 Highlighted View: 
               
            
           
           
               
               
               
               
               
            
               
                   
                   
                 Soda 
                   
                   
               
               
                   
                 Sales 
                 East 
                 West 
                 Central 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
            
               
                   
                 Qtr 1 
                 −9.32 
                 1.20 
                 20.6 
               
               
                   
                 Qtr 2 
                 1.34 
                 1.32 
                 1.56 
               
               
                   
                 Qtr 3 
                 1.60 
                 1.34 
                 1.21 
               
               
                   
                 Qtr 4 
                 1.81 
                 1.10 
                 30.4 
               
               
                   
                   
               
            
           
         
       
     
       FIG. 4  and element  400  illustrate the preferred method of the present invention that identifies data that are related to selected data  304  in a multidimensional database  110  on a computer system  500 . The method of the preferred embodiment of the present invention comprises obtaining selected data  304 , typically from a subject multidimensional database  126 , as shown in element  402 . The selected data  304  is obtained via the use of the index data cube  124 . As shown in element  404 , a view of the selected data  314  is obtained. A query of the index data cube  124  is created, as shown in element  406 , and as shown in element  408 , the query accesses the index data cube  124  to obtain related index data  310 . As shown in element  410 , a view of the identified related data  316  is obtained via the use of the related index data  310 . Elements  124  and  126  are described with reference to  FIG. 1 , elements  304 ,  306 ,  310 ,  314 , and  316  are described with reference to  FIG. 3 , and element  500  is are described with reference to  FIG. 5 . 
     Further, the view of the related data  316  and the selected data  314  may be formatted for presentation to a user, as shown in element  412 . The attributes of the related data  306  and the selected data  304  that meet the threshold set by highlight criteria  130  may be highlighted, as shown in element  414 . As shown in element  416 , the preferred embodiment of the present invention may also suppress irrelevant data  108  that exists in the subject database  126  that might otherwise be included as part of a view  212  showing selected data  304  in context. Elements  108  and  130  are described with reference to  FIG. 1 , and element  212  is described with reference to  FIG. 2 . 
       FIG. 5  is a block diagram of a computer system  500 , suitable for employment of the present invention. System  500  may be implemented on a general-purpose microcomputer, such as one of the members of the IBM Personal Computer family, or other conventional workstation or graphics computer devices, or mainframe computers. In its preferred embodiment, system  500  includes a user input device  510 , a display  515 , a printer  520 , a processor  555 , a read only memory (ROM)  550 , a data storage device  122 , such as a hard drive, a random access memory (RAM)  540 , and a storage media interface  535 , all of which are coupled to a bus  525  or other communication means for communicating information. Although system  500  is represented herein as a standalone system, it is not limited to such, but instead can be part of a networked system. For example, the computer system  500  may be connected locally or remotely to fixed or removable data storage devices  122  and data transmission devices  545 . Further, the computer system  500 , such as the server computer system  102  and the client computer system  104 , also could be connected to other computer systems  500  via the data transmission devices  545 . Elements  102  and  104  are described with reference to  FIG. 1 . 
     The RAM  540 , the data storage device  122 , and the ROM  550 , are memory components  558  that store data  108  and instructions for controlling the operation of processor  555 , which may be configured as a single processor or as a plurality of processors. The processor  555  executes a program  542  to perform the methods of the present invention, as described herein. Element  108  is described with reference to  FIG. 1 . 
     While the program  542  is indicated as loaded into the RAM  540 , it may be configured on a storage media  530  for subsequent loading into the data storage device  122 , the ROM  550 , or the RAM  540  via an appropriate storage media interface  535 . Storage media  530  can be any conventional storage media such as a magnetic tape, an optical storage media, a compact disk, or a floppy disk. Alternatively, storage media  530  can be a random access memory  540 , or other type of electronic storage, located on a remote storage system. 
     Generally, the computer programs and operating systems are all tangibly embodied in a computer-readable device or media, such as the memory  558 , the data storage device  122 , or the data transmission device  545 , thereby making an article of manufacture, such as a computer program product, according to the invention. As such, the terms “computer program product” as used herein are intended to encompass a computer program  542  accessible from any computer readable device or media. 
     Moreover, the computer programs  542  and operating systems are comprised of instructions which, when read and executed by the computer system  500 , such as the server computer system  102  and the client computer system  104 , cause the computer system  500 , such as the server computer system  102  and the client computer system  104 , to perform the steps necessary to implement and use the present invention. Under control of the operating system, the computer programs  542  may be loaded from the memory  558 , the data storage device  122 , or the data transmission devices  545  into the memories  558  of the computer system  500 , such as the server computer system  102  and the client computer system  104 , for use during actual operations. Those skilled in the art will recognize many modifications may be made to this configuration without departing from the scope of the present invention. Elements  102  and  104  are described with reference to  FIG. 1 . 
     The user input device  510 , such as a keyboard or speech recognition subsystem, enables a user to communicate information and command selections to the processor  555 . The user can observe information generated by the system  500  via the display  515  or the printer  520 . The user input device  510  may also be a device such as a mouse, track-ball, or joy-stick, which allows the user to manipulate a cursor on the display  515  for communicating additional information and command selections to the processor  555 . 
     When operating in accordance with one embodiment of the present invention, the system  500  identifies data  108  that is related to and associated with data  108  that has been selected from a multidimensional database  110 , by the use of an index that includes location and magnitude information about features in a subject multidimensional database. The processor  555  and the program  542  collectively operate as a module for identification of data  108  that is related to and associated with data  108  that has been selected from a multidimensional data  110  by the use of an index. The overwhelming amount of data  108  in a multidimensional database  110  is efficiently and advantageously reduced by the preferred embodiment of the present invention to the selected and related data. It will be appreciated that the present invention offers many advantages over prior art techniques. Elements  108  and  110  are described with reference to  FIG. 1 . 
     The present invention is typically implemented using one or more computer programs  542 , each of which executes under the control of an operating system and causes the computer system  500 , such as the server computer system  102  and the client computer system  104 , to perform the desired functions as described herein. Thus, using the present specification, the invention may be implemented as a machine, process, method, system, or article of manufacture by using standard programming and engineering techniques to produce software, firmware, hardware or any combination thereof. 
     It should be understood that various alternatives and modifications can be devised by those skilled in the art. However, these should not be viewed as limitations upon the practice of these teachings, as those skilled in the art, when guided by the foregoing teachings, may derive other suitable characteristics of a similar or different nature. The present invention is intended to embrace all such alternatives, modifications and variances that fall within the scope of the appended claims 
     Trademarks 
     IBM is a trademark or registered trademark of International Business machines, Corporation in the United States and other countries. 
     Essbase and Hyperion are trademarks or registered trademarks of Hyperion Solutions Corporation.