US20060190432A1 - System and method for graphically distinguishing levels of a multidimensional database - Google Patents
System and method for graphically distinguishing levels of a multidimensional database Download PDFInfo
- Publication number
- US20060190432A1 US20060190432A1 US11/063,469 US6346905A US2006190432A1 US 20060190432 A1 US20060190432 A1 US 20060190432A1 US 6346905 A US6346905 A US 6346905A US 2006190432 A1 US2006190432 A1 US 2006190432A1
- Authority
- US
- United States
- Prior art keywords
- levels
- dimension
- shades
- colors
- color
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
Definitions
- the technology described in this patent document relates generally to database operations, and more particularly to multidimensional database processing operations.
- Database technology has evolved to where multidimensional data can be stored and retrieved.
- data is categorized into measures which are numerical in nature and into dimensions (e.g., textual dimensions, time dimensions, etc.) which characterize the measures.
- the dimensions have hierarchies and have found utility in many fields, including the data warehousing field and the Online Analytical Processing (OLAP) field.
- OLAP Online Analytical Processing
- a method can be used with one or more processing devices in order to graphically distinguish levels of a multidimensional database and may include the steps of: receiving a query for retrieving data from the multidimensional database, wherein the multidimensional database contains a first dimension that is hierarchically arranged to categorize data in a plurality of levels; associating two or more of the plurality of levels in the first dimension with a plurality of colors; and generating display information based upon the results obtained through the query upon the multidimensional database.
- a system can be configured for graphically distinguishing levels from a multidimensional database and may include a server computer that includes the multidimensional database and a client computer.
- the client computer may be operable to retrieve data from the multidimensional database, and to execute a program for displaying information retrieved from the multidimensional database.
- the generated display information may be used to provide a graphical display that shows the query results categorized in at least the first dimension, wherein a plurality of colors are used to graphically distinguish the two or more levels in the first dimension.
- the colors may include different colors, shades of a single color, or shades of gray-scale.
- FIG. 1 is a block diagram depicting computer and software components for use with a multidimensional database and also depicting characteristics of the multidimensional database.
- FIG. 2 illustrates an example OLAP table for displaying data from an OLAP cube.
- FIG. 3 is a flow diagram showing an example method for graphically distinguishing levels in a multidimensional database, such as an OLAP cube.
- FIG. 4 is an example of an OLAP table that uses colors to graphically distinguish between the levels in two dimensions of an OLAP cube.
- FIG. 5 illustrates five example gradient schemes that may be used to graphically distinguish levels of a multidimensional database.
- FIG. 6 is another example of an OLAP table that uses colors to graphically distinguish between the levels in two dimensions of an OLAP cube.
- FIG. 7 is an example of an OLAP chart that uses shades of a color to graphically distinguish between the levels in a textual dimension of an OLAP cube.
- FIG. 8 is another example of an OLAP chart that uses shades of a color to graphically distinguish between the levels in a textual dimension of an OLAP cube.
- FIGS. 9A and 9B show an example OLAP chart that uses shades of a color to graphically distinguish between two dimensions in an OLAP cube.
- FIGS. 10A and 10B is an example of an OLAP chart that uses shades of a color to graphically distinguish between the levels in two dimensions of an OLAP cube.
- FIG. 11 is an example of an OLAP table that uses colors to graphically distinguish between the levels in three dimensions of an OLAP cube.
- FIG. 12 is another example of an OLAP table that uses colors to graphically distinguish between the levels in three dimensions of an OLAP cube.
- FIGS. 13 and 14 are examples OLAP tables that illustrate the use of a color gradient scheme in a breadcrumb.
- FIG. 15 is a block diagram of a system for graphically distinguishing levels of a multidimensional database.
- FIG. 1 depicts a system for use with a multidimensional database 2 .
- An end user 4 or a host application 6 (such as a data warehousing application, or an Online Analytical Processing (OLAP) or a data mining application) initiates a query to the multidimensional database 2 via a database engine 8 .
- the database engine 8 processes the query, such as by parsing and translating, optimizing, and evaluating in order to formulate a logical expression from the query.
- OLAP Online Analytical Processing
- the logical expression can be in the form of a plurality of terms connected via a plurality of operators such as logical operators, relational operators, arithmetic operators, etc.
- the engine 8 then scans the multidimensional database 2 to locate and relate data records 10 that fulfill the logical expression and return the results to the end user 4 or host application 6 .
- FIG. 1 further illustrates example characteristics of a multidimensional database 2 .
- data records 10 contain numerical measures 20 and dimensions (e.g., textual, time, etc.) 16 that characterize the numerical measures 20 .
- the multidimensional database 2 views data records 10 as cubes 12 that contain hierarchies of the dimensions 14 . Stated another way, a cube 12 represents a particular set of dimensions that the user can use to view the data records 10 .
- the dimensions 14 of the database's cubes are used for selecting and aggregating data at the desired level of detail.
- a dimension 16 is organized into a hierarchy composed of multiple levels 18 , each representing a level of detail required by the desired analysis.
- a dimension could contain geography information, time information, customer profile information, or other types of hierarchical data.
- the dimension could be arranged hierarchically starting from a general designation to a more specific one—e.g., a geography hierarchy could be arranged at a state level which could decompose to a county geographical level which could decompose even further, such as to a city or township geographical level.
- FIG. 2 illustrates an example OLAP table 30 for displaying data from an OLAP cube.
- the illustrated OLAP table 30 includes two example dimensions: a time dimension 32 and a geography dimension 34 .
- the time dimension 32 includes two levels, “year” 36 and “quarter” 38
- the geography dimension 34 includes three levels, “geography” 40 , “country” 42 and “region” 44 .
- the OLAP table 30 may be expanded or collapsed to show or hide the cells in a particular level.
- the year level “1994” in the time dimension 32 has been expanded (e.g., by selecting a “+” icon) to show the cells at the quarter level 38
- the year level “1993” has been collapsed (e.g., by selecting a “ ⁇ ” icon) to show only the cells at the year level 36 .
- the country level “USA” in the geography dimension 34 has been expanded to show the cells at the region level 44 (East, West and USA Total), and the country levels “Canada” and “Germany” have been collapsed to show only the cells at the country level 42 .
- FIG. 3 is a flow diagram showing an example method for graphically distinguishing levels in a multidimensional database, such as an OLAP cube.
- the method may, for example, begin when a database software application receives a query for retrieving data from the multidimensional database.
- the method determines the hierarchical structure for a first dimension of the data. For example, with reference to FIG. 2 , the method may determine that the time dimension 32 includes two levels 36 and 38 .
- gradients are applied to a pre-selected number (N) of the levels at step 52 , and colors are associated with each of the gradients at step 54 . For instance, different colors or different shades of a single color may be associated with each of the pre-selected number (N) of levels in the dimension.
- the levels of the dimension may be represented by progressively darker or progressively lighter shades of the color to illustrate the hierarchical nature of the data.
- the colors associated with each of the gradients may be different shades of gray-scale.
- step 56 the data is displayed (e.g., in an OLAP table) using the assigned colors to graphically distinguish between the levels of the dimension.
- a constant color may be assigned to the remaining levels at step 58 , and displayed at step 60 .
- the method may then be repeated, if necessary, to graphically distinguish the levels of additional dimensions of the data. For instance, two or more of the levels in a first dimension of the data may be graphically distinguished using shades of a first color, and two or more of the levels in a second dimension of the data may be graphically distinguished using shades of a second color.
- FIG. 4 is an example of an OLAP table 70 that uses colors to graphically distinguish between the levels in two dimensions of an OLAP cube.
- the OLAP table 70 illustrated in FIG. 4 includes the same data as the example 30 of FIG. 2 .
- the levels of the time and geography dimensions 72 , 74 are distinguished using a color gradient scheme.
- the levels in the geography dimension 74 are displayed using a first color (illustrated in the drawing by diagonal lines), and the levels in the time dimension 72 are displayed using a second color (illustrated in the drawing by horizontal lines).
- progressively darker shades of the color can be used to distinguish between levels in a dimension based on the depth of the level.
- Color shading is illustrated in the drawings by the spacing between the lines representing a color, with closer spacing representing a darker shade.
- the geographic-all level is the lightest shade of the first color
- the region level is the darkest shade of the first color
- the country level is a shade between the darkest and lightest.
- the year level is the lighter shade of the second color
- the quarter level is the darker shade of the second color.
- the illustrated Germany level has the same gradient (i.e., the same color shading) as the USA level, and is one shade lighter than the East and West levels, the user can be certain from the display that Germany is at the same level as USA and not a level above East and West.
- a dimension in the illustrated example 70 included additional levels, then increasingly darker shades of the color may be used to distinguish from higher levels. For example, if the geography dimension 74 included a level beyond region (e.g., state, county, city, etc.), then this additional level may be displayed with a darker shade of the first color than what was used to display the region level. In one example, progressively darker shades of a color may be associated with levels of a dimension up to a pre-determined level, and then all levels beyond the pre-determined level may be displayed using the same (e.g., darkest) shade of the color.
- a level beyond region e.g., state, county, city, etc.
- this additional level may be displayed with a darker shade of the first color than what was used to display the region level.
- progressively darker shades of a color may be associated with levels of a dimension up to a pre-determined level, and then all levels beyond the pre-determined level may be displayed using the same (e.g., darkest) shade of the color.
- FIG. 5 illustrates five example gradient schemes 75 - 79 that may be used to graphically distinguish levels of a multidimensional database.
- the levels in a dimension are distinguished using a color gradient scheme with shades of a single color.
- the levels are distinguished using progressively darker shades of the color
- the levels in the second example 76 are distinguished using progressively lighter shades of the color.
- the levels in a dimension are distinguished using different colors.
- the levels in a dimension are distinguished using shades of gray-scale (gray-scale is represented using a cross-hatch pattern).
- the levels are distinguished using progressively darker shades of gray-scale
- levels are distinguished using progressively lighter shades of gray-scale.
- FIG. 4 and FIGS. 6-15 use a color gradient scheme similar to the first two examples, 75 and 76 , shown in FIG. 5 , however, other examples could use different gradient schemes, such as the gradient schemes illustrated in examples 77 - 79 .
- FIG. 6 is another example of an OLAP table 80 that uses colors to graphically distinguish between the levels in two dimensions of an OLAP cube.
- the OLAP table 80 in this example is similar to the example 70 of FIG. 4 , except that the color gradient scheme progresses from dark to light, instead of light to dark. That is, the highest levels in a dimension are displayed using the darkest shade of a color, and deeper levels are distinguished using progressively lighter shades of the color.
- the levels of the geography dimension 84 are displayed using progressively lighter shades of a first color (illustrated by diagonal lines), and the levels of the time dimension 82 are displayed using progressively lighter shades of a second color (illustrated by horizontal lines).
- the geographic-all level is the darkest shade of the first color
- the region level is the lightest shade of the first color
- the country level is a shade between the darkest and lightest.
- a dimension in the example 80 of FIG. 6 included additional levels, then increasingly lighter shades may be used to distinguish from higher levels.
- progressively lighter shades of a color may be associated with levels up to a pre-determined level, and then all levels beyond the pre-determined level may be displayed using the same (e.g., lightest) shade of the color.
- FIG. 7 is an example of an OLAP chart 90 that uses shades of a color to graphically distinguish between the levels in a textual dimension of an OLAP cube.
- three levels 92 of a geographic dimension are included along the horizontal axis of the chart 90 , and the predicted sum for seven different levels ( 1 - 7 ) in the time dimension are illustrated by vertical bars ( 1 - 7 ) at each of the geography levels 92 .
- the levels in the geography dimension 92 are distinguished using progressively darker shades of a first color (illustrated by diagonal lines) based on the depth of the level.
- the darkest shade is used to display the region level (East and West)
- the lightest shade is used to display the all-geographic level
- a shade between darkest and lightest is used to display the country level (Canada, Germany and USA).
- the geographic dimension 92 in the example of FIG. 7 included additional levels, then increasingly darker shades may be used to distinguish from higher levels.
- progressively darker shades of a color may be associates with levels up to a pre-determined level, and then all levels beyond the pre-determined level may be displayed using the same (e.g., darkest) shade of the color.
- FIG. 8 is another example of an OLAP chart 100 that uses shades of a color to graphically distinguish between the levels in a textual dimension of an OLAP cube.
- the OLAP chart 100 in this example is similar to the example 90 of FIG. 7 , except that the color gradient scheme progresses from dark to light, instead of light to dark. That is, the highest level in the geographic dimension 102 is displayed using the darkest shade of the color, and deeper levels are displayed using progressively lighter shades of the color. Specifically, the geographic-all level is displayed using the darkest shade, the region level (East and West) is displayed using the lightest shade, and the country level (Canada, Germany and USA) is displayed using a shade between darkest and lightest.
- a dimension in the example 100 of FIG. 8 included additional levels, then increasingly lighter shades may be used to distinguish from higher levels.
- progressively lighter shades of a color may be associated with levels up to a pre-determined level, and then all levels beyond the pre-determined level may be displayed using the same (e.g., lightest) shade of the color.
- FIGS. 9A and 9B show an example OLAP chart 110 that uses shades of a color to graphically distinguish between two dimensions in an OLAP cube.
- two dimensions 112 , 114 are along the horizontal axis of the chart 110 : a consumer profile dimension 112 and a time dimension 114 .
- Data at different levels is presented in the chart 110 by the vertical bars (Total_Retail_PriceSum and Total_CostSum).
- the levels of the consumer profile dimension 112 are displayed using a first color shading and the levels of the time dimension 114 are displayed using a second color shading.
- the consumer profile dimension 112 is displayed using a lighter shade of a color and the time dimension 114 is displayed using a darker shade of the color. If additional dimensions were displayed along the horizontal axis of the chart 110 , then increasingly lighter or darker shades of the color may be used to distinguish the dimensions.
- FIGS. 10A and 10B are an example of an OLAP chart 120 that uses shades of a color to graphically distinguish between the levels in two dimensions of an OLAP cube.
- the OLAP chart 120 in this example is similar to the example 110 of FIG. 9 , except that the color gradient scheme is used to distinguish between each of the levels in the two dimensions displayed along the horizontal axis.
- the highest level displayed in the consumer profile dimension (gender) is displayed using the lightest shade of the color
- the next level displayed in the consumer profile dimension (age group) is displayed using the second lightest shade of the color.
- the levels of the time dimension 124 are displayed using shades of the color that are progressively darker than the shades used to display the customer profile dimension 122 . More particularly, the highest level displayed in the time dimension 124 (year) is displayed using the third lightest shade of the color, and the deepest displayed level of the time dimension 124 (quarter) is displayed using the darkest shade of the color.
- shades of different colors could be used to distinguish the levels in different dimensions displayed along the horizontal axis of the chart 120 .
- shades of a first color could be used to distinguish the levels of the time dimension 124 and shades of a second color could be used to distinguish the levels of the consumer profile dimension 122 .
- FIG. 11 is an example of an OLAP table 130 that uses colors to graphically distinguish between the levels in three dimensions of an OLAP cube.
- the OLAP table 130 in this example is similar to the example 70 of FIG. 4 , with the addition of a pay type dimension 132 displayed in the rows of the table 130 along with the geography dimension 134 .
- the levels of the pay type dimension 132 are graphically distinguished using progressively darker shades of the same color used for the geography dimension 134 . Specifically, the highest level of the pay type dimension 132 (pay type all) is displayed using the lightest shade of the color and the next displayed level (pay period) is displayed using the second lightest shade of the color.
- different colors may be used for each of the three dimensions 132 , 134 , 136 displayed on the table 130 , and/or the levels of a dimension 132 , 134 , 136 may be distinguished using progressively lighter shades of a color, instead of progressively darker shades. Also, if a dimension 132 , 134 , 136 in the illustrated example 130 included additional levels, then increasingly darker (or lighter) shades of the color may be used to distinguish from higher levels. In addition, progressively darker (or lighter) shades of a color may be associated with levels up to a pre-determined level, and then all levels beyond the pre-determined level may be displayed using the same (e.g., darkest or lightest) shade of the color.
- FIG. 12 is another example of an OLAP table 140 that uses colors to graphically distinguish between the levels in three dimensions 142 , 144 , 146 of an OLAP cube.
- the OLAP table 140 in this example is similar to the example 70 of FIG. 4 , with the addition of a pay type dimension 144 displayed in the columns of the table 140 along with the time dimension 142 .
- the levels of the pay type dimension 144 are graphically distinguished using progressively darker shades of the same color used for the time dimension 144 .
- the highest level of the pay type dimension 142 (pay type all) is displayed using the lightest shade of the color and the next displayed level (pay period) is displayed using the second lightest shade of the color.
- different colors may be used for each of the three dimensions 142 , 144 , 146 displayed on the table 140 , and/or the levels of a dimension 142 , 144 , 146 may be distinguished using progressively lighter shades of a color, instead of progressively darker shades. Also, if a dimension 142 , 144 , 146 in the illustrated example 140 included additional levels, then increasingly darker (or lighter) shades of the color may be used to distinguish from higher levels. In addition, progressively darker (or lighter) shades of a color may be associated with levels up to a pre-determined level, and then all levels beyond the pre-determined level may be displayed using the same (e.g., darkest or lightest) shade of the color.
- FIGS. 13 and 14 are example OLAP tables 150 , 160 that illustrate the use of a color gradient scheme in a breadcrumb.
- the OLAP table 150 illustrated in FIG. 13 is similar to the example 70 of FIG. 4 , except that the user has drilled into the year level of 1994 (e.g., by clicking on the “ 1994 ” header) to display only the quarter level 154 (the quarters from 1994 , specifically) on the table 150 .
- the two levels above the displayed quarter level 154 are shown in the breadcrumb 152 in order to provide the user with a reference for determining the depth of the data displayed on the table 150 .
- the levels in the dimension including both the displayed levels 154 and the levels in the breadcrumb 152 , are distinguished using progressively darker shades of a color.
- the color gradient scheme for a dimension extends from the levels in the breadcrumb 152 to the levels 154 displayed in the table.
- the first two levels in the time dimension time all and 1994
- the third level quarter is displayed in the table using the darkest shade of the color.
- the OLAP table 160 illustrated in FIG. 14 is similar to the example of FIG. 13 , except that the user has drilled into both the year level of 1994 and the geography level USA to display only the quarter level 166 for 1994 and the region level 168 (East and West) for USA.
- the time-all and year levels in the time dimension are displayed in a first breadcrumb 162
- the geographic-all and country levels of the geography dimension are displayed in a second breadcrumb 164 .
- the color gradient scheme used in the OLAP table is extended into the breadcrumbs.
- the first two levels are displayed in the first breadcrumb 162 using the two lightest shades of a first color
- the third level is displayed in the table using the darkest shade of the first color
- the first two levels are displayed in the second breadcrumb 164
- the third level is displayed in the table using the darkest shade of the second color.
- progressively darker shades of a color may be used to distinguish levels in a dimension, both in the table and in a breadcrumb, instead of progressively darker shades.
- progressively darker (or lighter) shades of a color may be associated with levels up to a pre-determined level, and then all levels beyond the pre-determined level may be displayed using the same (e.g., darkest or lightest) shade of the color.
- FIG. 15 is a block diagram of a system 170 for graphically distinguishing levels of a multidimensional database.
- the system 170 includes a server computer 172 and a client computer 174 .
- the client computer 174 includes a computer screen 176 and one or more input devices 178 , 180 .
- the multidimensional database 182 is stored on the server computer 172 and is accessible by a software application 184 executed on the client computer 174 .
- the software application 184 may, for example, execute computer instructions to perform the method described above with reference to FIG. 3 .
- a user interface 186 generated by the software application 184 is displayed on the computer screen 176 .
- the user interface 186 may display an OLAP table or an OLAP chart that uses a gradient scheme to distinguish levels from the multidimensional database 182 , as described herein
- systems and methods described herein may be implemented on various types of computer architectures, such as for example on a single general purpose computer or workstation, or on a networked system, or in a client-server configuration, or in an application service provider configuration.
- systems and methods may include data signals conveyed via networks (e.g., local area network, wide area network, internet, etc.), fiber optic medium, carrier waves, wireless networks, etc. for communication with one or more data processing devices.
- the data signals can carry any or all of the data disclosed herein that is provided to or from a device.
- the methods and systems described herein may be implemented on many different types of processing devices by program code comprising program instructions that are executable by the device processing subsystem.
- the software program instructions may include source code, object code, machine code, or any other stored data that is operable to cause a processing system to perform methods described herein.
- Other implementations may also be used, however, such as firmware or even appropriately designed hardware configured to carry out the methods and systems described herein.
- the systems' and methods' data may be stored and implemented in one or more different types of computer-implemented ways, such as different types of storage devices and programming constructs (e.g., data stores, RAM, ROM, Flash memory, flat files, databases, programming data structures, programming variables, IF-THEN (or similar type) statement constructs, etc.).
- storage devices and programming constructs e.g., data stores, RAM, ROM, Flash memory, flat files, databases, programming data structures, programming variables, IF-THEN (or similar type) statement constructs, etc.
- data structures describe formats for use in organizing and storing data in databases, programs, memory, or other computer-readable media for use by a computer program.
- the systems and methods may be provided on many different types of computer-readable media including computer storage mechanisms (e.g., CD-ROM, diskette, RAM, flash memory, computer's hard drive, etc.) that contain instructions for use in execution by a processor to perform the methods' operations and implement the systems described herein.
- computer storage mechanisms e.g., CD-ROM, diskette, RAM, flash memory, computer's hard drive, etc.
- a module or processor includes but is not limited to a unit of code that performs a software operation, and can be implemented for example as a subroutine unit of code, or as a software function unit of code, or as an object (as in an object-oriented paradigm), or as an applet, or in a computer script language, or as another type of computer code.
- the software components and/or functionality may be located on a single computer or distributed across multiple computers depending upon the situation at hand.
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Instructional Devices (AREA)
- User Interface Of Digital Computer (AREA)
- Processing Or Creating Images (AREA)
Abstract
In accordance with the teachings described herein, systems and methods are provided for graphically distinguishing levels from a multidimensional database. Levels from a multidimensional database are distinguished, such as by associating two or more of database's levels with a plurality of different visual indicators.
Description
- The technology described in this patent document relates generally to database operations, and more particularly to multidimensional database processing operations.
- Database technology has evolved to where multidimensional data can be stored and retrieved. In a multidimensional database, data is categorized into measures which are numerical in nature and into dimensions (e.g., textual dimensions, time dimensions, etc.) which characterize the measures. The dimensions have hierarchies and have found utility in many fields, including the data warehousing field and the Online Analytical Processing (OLAP) field. Despite the evolution of database technology, data visualization of multidimensional data has remained limited.
- In accordance with the teachings described herein, systems and methods are provided for graphically distinguishing levels of a multidimensional database. As an example, a method can be used with one or more processing devices in order to graphically distinguish levels of a multidimensional database and may include the steps of: receiving a query for retrieving data from the multidimensional database, wherein the multidimensional database contains a first dimension that is hierarchically arranged to categorize data in a plurality of levels; associating two or more of the plurality of levels in the first dimension with a plurality of colors; and generating display information based upon the results obtained through the query upon the multidimensional database. As another example, a system can be configured for graphically distinguishing levels from a multidimensional database and may include a server computer that includes the multidimensional database and a client computer. The client computer may be operable to retrieve data from the multidimensional database, and to execute a program for displaying information retrieved from the multidimensional database. The generated display information may be used to provide a graphical display that shows the query results categorized in at least the first dimension, wherein a plurality of colors are used to graphically distinguish the two or more levels in the first dimension. The colors may include different colors, shades of a single color, or shades of gray-scale.
-
FIG. 1 is a block diagram depicting computer and software components for use with a multidimensional database and also depicting characteristics of the multidimensional database. -
FIG. 2 illustrates an example OLAP table for displaying data from an OLAP cube. -
FIG. 3 is a flow diagram showing an example method for graphically distinguishing levels in a multidimensional database, such as an OLAP cube. -
FIG. 4 is an example of an OLAP table that uses colors to graphically distinguish between the levels in two dimensions of an OLAP cube. -
FIG. 5 illustrates five example gradient schemes that may be used to graphically distinguish levels of a multidimensional database. -
FIG. 6 is another example of an OLAP table that uses colors to graphically distinguish between the levels in two dimensions of an OLAP cube. -
FIG. 7 is an example of an OLAP chart that uses shades of a color to graphically distinguish between the levels in a textual dimension of an OLAP cube. -
FIG. 8 is another example of an OLAP chart that uses shades of a color to graphically distinguish between the levels in a textual dimension of an OLAP cube. -
FIGS. 9A and 9B show an example OLAP chart that uses shades of a color to graphically distinguish between two dimensions in an OLAP cube. -
FIGS. 10A and 10B is an example of an OLAP chart that uses shades of a color to graphically distinguish between the levels in two dimensions of an OLAP cube. -
FIG. 11 is an example of an OLAP table that uses colors to graphically distinguish between the levels in three dimensions of an OLAP cube. -
FIG. 12 is another example of an OLAP table that uses colors to graphically distinguish between the levels in three dimensions of an OLAP cube. -
FIGS. 13 and 14 are examples OLAP tables that illustrate the use of a color gradient scheme in a breadcrumb. -
FIG. 15 is a block diagram of a system for graphically distinguishing levels of a multidimensional database. -
FIG. 1 depicts a system for use with amultidimensional database 2. Anend user 4 or a host application 6 (such as a data warehousing application, or an Online Analytical Processing (OLAP) or a data mining application) initiates a query to themultidimensional database 2 via adatabase engine 8. Thedatabase engine 8 processes the query, such as by parsing and translating, optimizing, and evaluating in order to formulate a logical expression from the query. - The logical expression can be in the form of a plurality of terms connected via a plurality of operators such as logical operators, relational operators, arithmetic operators, etc. The
engine 8 then scans themultidimensional database 2 to locate and relatedata records 10 that fulfill the logical expression and return the results to theend user 4 orhost application 6. -
FIG. 1 further illustrates example characteristics of amultidimensional database 2. In amultidimensional database 2,data records 10 containnumerical measures 20 and dimensions (e.g., textual, time, etc.) 16 that characterize thenumerical measures 20. Themultidimensional database 2views data records 10 ascubes 12 that contain hierarchies of thedimensions 14. Stated another way, acube 12 represents a particular set of dimensions that the user can use to view thedata records 10. - The
dimensions 14 of the database's cubes are used for selecting and aggregating data at the desired level of detail. Adimension 16 is organized into a hierarchy composed ofmultiple levels 18, each representing a level of detail required by the desired analysis. For example, a dimension could contain geography information, time information, customer profile information, or other types of hierarchical data. The dimension could be arranged hierarchically starting from a general designation to a more specific one—e.g., a geography hierarchy could be arranged at a state level which could decompose to a county geographical level which could decompose even further, such as to a city or township geographical level. - A table with nested cells may be used to display data within an
OLAP cube 12. The nested cells of the table can be structured such that the presence and absence of division lines represent the hierarchical nature of the data.FIG. 2 illustrates an example OLAP table 30 for displaying data from an OLAP cube. The illustrated OLAP table 30 includes two example dimensions: atime dimension 32 and ageography dimension 34. Thetime dimension 32 includes two levels, “year” 36 and “quarter” 38, and thegeography dimension 34 includes three levels, “geography” 40, “country” 42 and “region” 44. - As illustrated, the OLAP table 30 may be expanded or collapsed to show or hide the cells in a particular level. For instance, in the illustrated example, the year level “1994” in the
time dimension 32 has been expanded (e.g., by selecting a “+” icon) to show the cells at thequarter level 38, and the year level “1993” has been collapsed (e.g., by selecting a “−” icon) to show only the cells at theyear level 36. Also, the country level “USA” in thegeography dimension 34 has been expanded to show the cells at the region level 44 (East, West and USA Total), and the country levels “Canada” and “Germany” have been collapsed to show only the cells at thecountry level 42. However, because data may be expanded or collapsed at different levels within the hierarchy of the OLAP table 30, it may often be difficult to ascertain which cells are at the same level. For example, without the geographic knowledge, it may be difficult to determine by looking at the table 30 whether Germany is at the same level as USA or East and West. -
FIG. 3 is a flow diagram showing an example method for graphically distinguishing levels in a multidimensional database, such as an OLAP cube. The method may, for example, begin when a database software application receives a query for retrieving data from the multidimensional database. Atstep 50, the method determines the hierarchical structure for a first dimension of the data. For example, with reference toFIG. 2 , the method may determine that thetime dimension 32 includes twolevels step 52, and colors are associated with each of the gradients atstep 54. For instance, different colors or different shades of a single color may be associated with each of the pre-selected number (N) of levels in the dimension. If shades of a single color are used, then the levels of the dimension may be represented by progressively darker or progressively lighter shades of the color to illustrate the hierarchical nature of the data. In another example, the colors associated with each of the gradients may be different shades of gray-scale. - In
step 56, the data is displayed (e.g., in an OLAP table) using the assigned colors to graphically distinguish between the levels of the dimension. In addition, if the hierarchical structure of the dimension includes more than the pre-selected number (N) of levels, then a constant color may be assigned to the remaining levels atstep 58, and displayed atstep 60. The method may then be repeated, if necessary, to graphically distinguish the levels of additional dimensions of the data. For instance, two or more of the levels in a first dimension of the data may be graphically distinguished using shades of a first color, and two or more of the levels in a second dimension of the data may be graphically distinguished using shades of a second color. -
FIG. 4 is an example of an OLAP table 70 that uses colors to graphically distinguish between the levels in two dimensions of an OLAP cube. The OLAP table 70 illustrated inFIG. 4 includes the same data as the example 30 ofFIG. 2 . However, in this example, the levels of the time andgeography dimensions geography dimension 74 are displayed using a first color (illustrated in the drawing by diagonal lines), and the levels in thetime dimension 72 are displayed using a second color (illustrated in the drawing by horizontal lines). - In order to convey the hierarchical nature of the data, progressively darker shades of the color can be used to distinguish between levels in a dimension based on the depth of the level. Color shading is illustrated in the drawings by the spacing between the lines representing a color, with closer spacing representing a darker shade. For example, in the
geography dimension 74 the geographic-all level is the lightest shade of the first color, the region level is the darkest shade of the first color, and the country level is a shade between the darkest and lightest. Similarly, in thetime dimension 72 the year level is the lighter shade of the second color and the quarter level is the darker shade of the second color. In this manner, a user can ascertain from the color of the display the depth of the data in the overall OLAP cube structure. For instance, because the illustrated Germany level has the same gradient (i.e., the same color shading) as the USA level, and is one shade lighter than the East and West levels, the user can be certain from the display that Germany is at the same level as USA and not a level above East and West. - If a dimension in the illustrated example 70 included additional levels, then increasingly darker shades of the color may be used to distinguish from higher levels. For example, if the
geography dimension 74 included a level beyond region (e.g., state, county, city, etc.), then this additional level may be displayed with a darker shade of the first color than what was used to display the region level. In one example, progressively darker shades of a color may be associated with levels of a dimension up to a pre-determined level, and then all levels beyond the pre-determined level may be displayed using the same (e.g., darkest) shade of the color. -
FIG. 5 illustrates five example gradient schemes 75-79 that may be used to graphically distinguish levels of a multidimensional database. In the first two examples 75 and 76, the levels in a dimension are distinguished using a color gradient scheme with shades of a single color. In the first example 75, the levels are distinguished using progressively darker shades of the color, and in the second example 76, the levels are distinguished using progressively lighter shades of the color. In the third example 77, the levels in a dimension are distinguished using different colors. In the last two examples 78 and 79, the levels in a dimension are distinguished using shades of gray-scale (gray-scale is represented using a cross-hatch pattern). Specifically, in the fourth example 78 the levels are distinguished using progressively darker shades of gray-scale, and in the fifth example 79, levels are distinguished using progressively lighter shades of gray-scale. The examples illustrated inFIG. 4 andFIGS. 6-15 use a color gradient scheme similar to the first two examples, 75 and 76, shown inFIG. 5 , however, other examples could use different gradient schemes, such as the gradient schemes illustrated in examples 77-79. -
FIG. 6 is another example of an OLAP table 80 that uses colors to graphically distinguish between the levels in two dimensions of an OLAP cube. The OLAP table 80 in this example is similar to the example 70 ofFIG. 4 , except that the color gradient scheme progresses from dark to light, instead of light to dark. That is, the highest levels in a dimension are displayed using the darkest shade of a color, and deeper levels are distinguished using progressively lighter shades of the color. In the illustrated example 80, the levels of thegeography dimension 84 are displayed using progressively lighter shades of a first color (illustrated by diagonal lines), and the levels of thetime dimension 82 are displayed using progressively lighter shades of a second color (illustrated by horizontal lines). For example, in thegeography dimension 84 the geographic-all level is the darkest shade of the first color, the region level is the lightest shade of the first color, and the country level is a shade between the darkest and lightest. - If a dimension in the example 80 of
FIG. 6 included additional levels, then increasingly lighter shades may be used to distinguish from higher levels. In addition, progressively lighter shades of a color may be associated with levels up to a pre-determined level, and then all levels beyond the pre-determined level may be displayed using the same (e.g., lightest) shade of the color. -
FIG. 7 is an example of anOLAP chart 90 that uses shades of a color to graphically distinguish between the levels in a textual dimension of an OLAP cube. In the illustratedOLAP chart 90, threelevels 92 of a geographic dimension are included along the horizontal axis of thechart 90, and the predicted sum for seven different levels (1-7) in the time dimension are illustrated by vertical bars (1-7) at each of thegeography levels 92. The levels in thegeography dimension 92 are distinguished using progressively darker shades of a first color (illustrated by diagonal lines) based on the depth of the level. In the illustrated example, the darkest shade is used to display the region level (East and West), the lightest shade is used to display the all-geographic level, and a shade between darkest and lightest is used to display the country level (Canada, Germany and USA). - If the
geographic dimension 92 in the example ofFIG. 7 included additional levels, then increasingly darker shades may be used to distinguish from higher levels. In addition, progressively darker shades of a color may be associates with levels up to a pre-determined level, and then all levels beyond the pre-determined level may be displayed using the same (e.g., darkest) shade of the color. -
FIG. 8 is another example of anOLAP chart 100 that uses shades of a color to graphically distinguish between the levels in a textual dimension of an OLAP cube. TheOLAP chart 100 in this example is similar to the example 90 ofFIG. 7 , except that the color gradient scheme progresses from dark to light, instead of light to dark. That is, the highest level in thegeographic dimension 102 is displayed using the darkest shade of the color, and deeper levels are displayed using progressively lighter shades of the color. Specifically, the geographic-all level is displayed using the darkest shade, the region level (East and West) is displayed using the lightest shade, and the country level (Canada, Germany and USA) is displayed using a shade between darkest and lightest. - If a dimension in the example 100 of
FIG. 8 included additional levels, then increasingly lighter shades may be used to distinguish from higher levels. In addition, progressively lighter shades of a color may be associated with levels up to a pre-determined level, and then all levels beyond the pre-determined level may be displayed using the same (e.g., lightest) shade of the color. -
FIGS. 9A and 9B show anexample OLAP chart 110 that uses shades of a color to graphically distinguish between two dimensions in an OLAP cube. In this example, twodimensions consumer profile dimension 112 and atime dimension 114. Data at different levels is presented in thechart 110 by the vertical bars (Total_Retail_PriceSum and Total_CostSum). In order to distinguish between the twodimensions chart 110, the levels of theconsumer profile dimension 112 are displayed using a first color shading and the levels of thetime dimension 114 are displayed using a second color shading. In the illustrated example, theconsumer profile dimension 112 is displayed using a lighter shade of a color and thetime dimension 114 is displayed using a darker shade of the color. If additional dimensions were displayed along the horizontal axis of thechart 110, then increasingly lighter or darker shades of the color may be used to distinguish the dimensions. -
FIGS. 10A and 10B are an example of anOLAP chart 120 that uses shades of a color to graphically distinguish between the levels in two dimensions of an OLAP cube. TheOLAP chart 120 in this example is similar to the example 110 ofFIG. 9 , except that the color gradient scheme is used to distinguish between each of the levels in the two dimensions displayed along the horizontal axis. The highest level displayed in the consumer profile dimension (gender) is displayed using the lightest shade of the color, and the next level displayed in the consumer profile dimension (age group) is displayed using the second lightest shade of the color. The levels of thetime dimension 124 are displayed using shades of the color that are progressively darker than the shades used to display thecustomer profile dimension 122. More particularly, the highest level displayed in the time dimension 124 (year) is displayed using the third lightest shade of the color, and the deepest displayed level of the time dimension 124 (quarter) is displayed using the darkest shade of the color. - If additional levels were displayed along the horizontal axis of the
chart 120, then increasingly lighter or darker shades of the color may be used. In other examples, shades of different colors could be used to distinguish the levels in different dimensions displayed along the horizontal axis of thechart 120. For example, shades of a first color could be used to distinguish the levels of thetime dimension 124 and shades of a second color could be used to distinguish the levels of theconsumer profile dimension 122. -
FIG. 11 is an example of an OLAP table 130 that uses colors to graphically distinguish between the levels in three dimensions of an OLAP cube. The OLAP table 130 in this example is similar to the example 70 ofFIG. 4 , with the addition of apay type dimension 132 displayed in the rows of the table 130 along with thegeography dimension 134. In this example, the levels of thepay type dimension 132 are graphically distinguished using progressively darker shades of the same color used for thegeography dimension 134. Specifically, the highest level of the pay type dimension 132 (pay type all) is displayed using the lightest shade of the color and the next displayed level (pay period) is displayed using the second lightest shade of the color. - In other examples, different colors may be used for each of the three
dimensions dimension dimension -
FIG. 12 is another example of an OLAP table 140 that uses colors to graphically distinguish between the levels in threedimensions FIG. 4 , with the addition of apay type dimension 144 displayed in the columns of the table 140 along with thetime dimension 142. In this example, the levels of thepay type dimension 144 are graphically distinguished using progressively darker shades of the same color used for thetime dimension 144. Specifically, the highest level of the pay type dimension 142 (pay type all) is displayed using the lightest shade of the color and the next displayed level (pay period) is displayed using the second lightest shade of the color. - In other examples, different colors may be used for each of the three
dimensions dimension dimension -
FIGS. 13 and 14 are example OLAP tables 150, 160 that illustrate the use of a color gradient scheme in a breadcrumb. The OLAP table 150 illustrated inFIG. 13 is similar to the example 70 ofFIG. 4 , except that the user has drilled into the year level of 1994 (e.g., by clicking on the “1994” header) to display only the quarter level 154 (the quarters from 1994, specifically) on the table 150. The two levels above the displayedquarter level 154 are shown in thebreadcrumb 152 in order to provide the user with a reference for determining the depth of the data displayed on the table 150. The levels in the dimension, including both the displayedlevels 154 and the levels in thebreadcrumb 152, are distinguished using progressively darker shades of a color. In addition, the color gradient scheme for a dimension extends from the levels in thebreadcrumb 152 to thelevels 154 displayed in the table. In the illustrated example 150, the first two levels in the time dimension (time all and 1994) are displayed in thebreadcrumb 152 using the two lightest shades of a color, and the third level (quarter) is displayed in the table using the darkest shade of the color. - The OLAP table 160 illustrated in
FIG. 14 is similar to the example ofFIG. 13 , except that the user has drilled into both the year level of 1994 and the geography level USA to display only thequarter level 166 for 1994 and the region level 168 (East and West) for USA. The time-all and year levels in the time dimension are displayed in afirst breadcrumb 162, and the geographic-all and country levels of the geography dimension are displayed in asecond breadcrumb 164. Similar toFIG. 13 , the color gradient scheme used in the OLAP table is extended into the breadcrumbs. In the time dimension, the first two levels (time all and 1994) are displayed in thefirst breadcrumb 162 using the two lightest shades of a first color, and the third level (quarter) is displayed in the table using the darkest shade of the first color. In the geography dimension, the first two levels (geographic all and USA) are displayed in thesecond breadcrumb 164, and the third level (East and West) is displayed in the table using the darkest shade of the second color. - In both
FIGS. 13 and 14 , additional levels in a dimension could be illustrated using progressively darker shades of the color. In other examples, progressively lighter shades of a color (or gray-scale) may be used to distinguish levels in a dimension, both in the table and in a breadcrumb, instead of progressively darker shades. In addition, progressively darker (or lighter) shades of a color may be associated with levels up to a pre-determined level, and then all levels beyond the pre-determined level may be displayed using the same (e.g., darkest or lightest) shade of the color. -
FIG. 15 is a block diagram of asystem 170 for graphically distinguishing levels of a multidimensional database. Thesystem 170 includes aserver computer 172 and aclient computer 174. Theclient computer 174 includes acomputer screen 176 and one ormore input devices multidimensional database 182 is stored on theserver computer 172 and is accessible by asoftware application 184 executed on theclient computer 174. Thesoftware application 184 may, for example, execute computer instructions to perform the method described above with reference toFIG. 3 . Auser interface 186 generated by thesoftware application 184 is displayed on thecomputer screen 176. Theuser interface 186 may display an OLAP table or an OLAP chart that uses a gradient scheme to distinguish levels from themultidimensional database 182, as described herein - This written description uses examples to disclose the invention, including the best mode, and also to enable a person skilled in the art to make and use the invention. The patentable scope of the invention may include other examples that occur to those skilled in the art. For instance, other examples may use a graphical line pattern (e.g., horizontal lines, diagonal lines, vertical lines, cross-hatch, etc.), instead of color or gray-scale, to distinguish levels from a multidimensional database. Levels within a dimension may, for example, be distinguished using increasingly more-dense or less-dense iterations of the line pattern (similar to the way in which color shading is represented in the attached drawing figures).
- It is further noted that the systems and methods described herein may be implemented on various types of computer architectures, such as for example on a single general purpose computer or workstation, or on a networked system, or in a client-server configuration, or in an application service provider configuration.
- It is further noted that the systems and methods may include data signals conveyed via networks (e.g., local area network, wide area network, internet, etc.), fiber optic medium, carrier waves, wireless networks, etc. for communication with one or more data processing devices. The data signals can carry any or all of the data disclosed herein that is provided to or from a device.
- Additionally, the methods and systems described herein may be implemented on many different types of processing devices by program code comprising program instructions that are executable by the device processing subsystem. The software program instructions may include source code, object code, machine code, or any other stored data that is operable to cause a processing system to perform methods described herein. Other implementations may also be used, however, such as firmware or even appropriately designed hardware configured to carry out the methods and systems described herein.
- The systems' and methods' data (e.g., associations, mappings, etc.) may be stored and implemented in one or more different types of computer-implemented ways, such as different types of storage devices and programming constructs (e.g., data stores, RAM, ROM, Flash memory, flat files, databases, programming data structures, programming variables, IF-THEN (or similar type) statement constructs, etc.). It is noted that data structures describe formats for use in organizing and storing data in databases, programs, memory, or other computer-readable media for use by a computer program.
- The systems and methods may be provided on many different types of computer-readable media including computer storage mechanisms (e.g., CD-ROM, diskette, RAM, flash memory, computer's hard drive, etc.) that contain instructions for use in execution by a processor to perform the methods' operations and implement the systems described herein.
- The computer components, software modules, functions, data stores and data structures described herein may be connected directly or indirectly to each other in order to allow the flow of data needed for their operations. It is also noted that a module or processor includes but is not limited to a unit of code that performs a software operation, and can be implemented for example as a subroutine unit of code, or as a software function unit of code, or as an object (as in an object-oriented paradigm), or as an applet, or in a computer script language, or as another type of computer code. The software components and/or functionality may be located on a single computer or distributed across multiple computers depending upon the situation at hand.
Claims (38)
1. A computer-implemented method for displaying data retrieved from a multidimensional database, comprising:
receiving a query for retrieving data from the multidimensional database;
wherein the multidimensional database contains a first dimension that is hierarchically arranged to categorize data in a plurality of levels;
associating two or more of the plurality of levels in the first dimension with a plurality of colors;
generating display information based upon the results obtained through the query upon the multidimensional database;
wherein the generated display information is for use in providing a graphical display that shows the query results categorized in at least the first dimension, and wherein the colors are displayed to graphically distinguish the two or more levels in the first dimension.
2. The method of claim 1 , wherein the plurality of colors are shades of a single color.
3. The method of claim 2 , wherein the two or more levels in the first dimension are distinguished using progressively darker shades of the single color.
4. The method of claim 2 , wherein the two or more levels in the first dimension are distinguished using progressively lighter shades of the single color.
5. The method of claim 1 , wherein the plurality of colors are shades of a gray-scale.
6. The method of claim 5 , wherein the two or more levels in the first dimension are distinguished using progressively darker shades of gray-scale.
7. The method of claim 5 , wherein the two or more levels in the first dimension are distinguished using progressively lighter shades of gray-scale.
8. The method of claim 1 , wherein the multidimensional database contains a second dimension that is hierarchically arranged to categorize data in a second plurality of levels, further comprising:
associating two or more of the second plurality of levels in the second dimension with a second plurality of colors;
wherein the graphical display shows the query results categorized in at least the first dimension and the second dimension, and wherein the plurality of colors are displayed to graphically distinguish the two or more levels in the first dimension and the second plurality of colors are displayed to graphically distinguish the two or more levels in the second dimension.
9. The method of claim 8 , wherein the plurality of colors are shades of a first color and the second plurality of colors are shades of a second color.
10. The method of claim 8 , wherein the plurality of colors are the same as the second plurality of colors.
11. The method of claim 8 , wherein at least one of the plurality of colors and the second plurality of colors are shades of a single color.
12. The method of claim 8 , wherein at least one of the plurality of colors and the second plurality of colors are shades of gray-scale.
13. The method of claim 8 , wherein the plurality of colors and the second plurality of colors are shades of the same color.
14. The method of claim 9 , wherein the two or more levels in the first dimension are distinguished using progressively darker shades of the first color and the two or more levels in the second dimension are distinguished using progressively darker shades of the second color.
15. The method of claim 9 , wherein the two or more levels in the first dimension are distinguished using progressively lighter shades of the first color and the two or more levels in the second dimension are distinguished using progressively lighter shades of the second color.
16. The method of claim 3 , wherein the progressively darker shades are used to display levels in the first dimension through a pre-selected level, and wherein a darkest of the progressively darker shades is used to display levels in the first dimension deeper than the pre-selected level.
17. The method of claim 3 , wherein the progressively lighter shades are used to display levels in the first dimension through a pre-selected level, and wherein a lightest of the progressively lighter shades is used to display levels in the first dimension deeper than the pre-selected level.
18. The method of claim 1 , wherein the graphical display includes an online analytical processing (OLAP) table.
19. The method of claim 1 , wherein the graphical display includes an online analytical processing (OLAP) chart.
20. The method of clam 18, wherein one or more of the levels in the first dimension are displayed in a breadcrumb, and wherein the colors graphically distinguish the one or more levels displayed in the breadcrumb.
21. A computer program stored on a computer readable medium and executable by a computer, when executed by the computer, the computer program being operable to perform the method steps of claim 1 .
22. A system for use with a multidimensional database containing a first dimension that is hierarchically arranged to categorize data in a plurality of levels, comprising:
data retrieval software instructions configured to retrieve data from the multidimensional database;
color gradient software instructions configured to associate two or more of the plurality of levels in the first dimension of the retrieved data with a plurality of colors; and
user interface software instructions configured to generate a graphical display that shows the retrieved data categorized in at least the first dimension, wherein the plurality of colors are displayed to graphically distinguish the two or more levels in the first dimension.
23. The system of claim 22 , wherein the plurality of colors are shades of a single color.
24. The system of claim 23 , wherein the graphical display distinguishes the two or more levels in the first dimension using progressively darker or progressively lighter shades of the single color.
25. The system of claim 22 , wherein the plurality of colors are gray-scale.
26. The system of claim 25 , wherein the graphical display distinguishes the two or more levels in the first dimension using progressively darker or progressively lighter shades of gray-scale.
27. The system of claim 22 , wherein the multidimensional database contains a second dimension that is hierarchically arranged to categorize data in a second plurality of levels, and wherein the color gradient software instructions are further configured to:
associate two or more of the second plurality of levels in the second dimension of the retrieved data with a second plurality of colors;
wherein the graphical display shows the retrieved data categorized in at least the first dimension and the second dimension, and wherein the plurality of colors are displayed to graphically distinguish the two or more levels in the first dimension and the second plurality of colors are displayed to graphically distinguish the two or more levels in the second dimension.
28. The system of claim 27 , wherein the plurality of colors are shades of a first color and the second plurality of colors are shades of a second color.
29. The system of claim 28 , wherein the graphical display distinguishes the two or more levels in the first dimension using progressively darker or progressively lighter shades of the first color and distinguishes the two or more levels in the second dimension using progressively darker or progressively lighter shades of the second color.
30. The system of claim 27 , wherein at least one of the plurality of colors and the second plurality of colors are shades of gray-scale.
31. The system of claim 30 , wherein the graphical display distinguishes the two or more levels in at least one of the first dimension and the second dimension using progressively darker or progressively lighter shades of gray-scale.
32. The system of claim 22 , wherein the graphical display includes an online analytical processing (OLAP) table.
33. The system of claim 22 , wherein the graphical display includes an online analytical processing (OLAP) chart.
34. A computer-implemented system for displaying data retrieved from a multidimensional database, comprising:
means for receiving a query for retrieving data from the multidimensional database;
wherein the multidimensional database contains a first dimension that is hierarchically arranged to categorize data in a plurality of levels;
means for associating two or more of the plurality of levels in the first dimension with gradients representing a depth of the level in the first dimension of the multidimensional database;
means for generating display information based upon the results obtained through the query upon the multidimensional database;
wherein the generated display information is for use in providing a graphical display that shows the query results categorized in at least the first dimension, and wherein the gradients distinguish the two or more levels in the first dimension.
35. The method of claim 34 , wherein the gradients are graphically represented using shades of a single color, wherein progressively darker or progressively lighter shades of the color are used to distinguish between the two or more levels.
36. The method of claim 34 , wherein the gradients are graphically represented using a plurality of different colors.
37. The method of claim 34 , wherein the gradients are graphically represented using shaded of gray-scale, wherein progressively darker or progressively lighter shades of gray-scale are used to distinguish between the two or more levels.
38. The method of claim 34 , wherein the gradients are graphically represented using a line pattern, wherein progressively more-dense or progressively less-dense iterations of the line pattern are used to distinguish between the two or more levels.
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/063,469 US20060190432A1 (en) | 2005-02-22 | 2005-02-22 | System and method for graphically distinguishing levels of a multidimensional database |
US13/008,413 US7996378B2 (en) | 2005-02-22 | 2011-01-18 | System and method for graphically distinguishing levels of a multidimensional database |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US11/063,469 US20060190432A1 (en) | 2005-02-22 | 2005-02-22 | System and method for graphically distinguishing levels of a multidimensional database |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/008,413 Continuation US7996378B2 (en) | 2005-02-22 | 2011-01-18 | System and method for graphically distinguishing levels of a multidimensional database |
Publications (1)
Publication Number | Publication Date |
---|---|
US20060190432A1 true US20060190432A1 (en) | 2006-08-24 |
Family
ID=36914033
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US11/063,469 Abandoned US20060190432A1 (en) | 2005-02-22 | 2005-02-22 | System and method for graphically distinguishing levels of a multidimensional database |
US13/008,413 Active US7996378B2 (en) | 2005-02-22 | 2011-01-18 | System and method for graphically distinguishing levels of a multidimensional database |
Family Applications After (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/008,413 Active US7996378B2 (en) | 2005-02-22 | 2011-01-18 | System and method for graphically distinguishing levels of a multidimensional database |
Country Status (1)
Country | Link |
---|---|
US (2) | US20060190432A1 (en) |
Cited By (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060107289A1 (en) * | 2004-07-28 | 2006-05-18 | Microsoft Corporation | Thumbnail generation and presentation for recorded TV programs |
US20080270848A1 (en) * | 2007-04-26 | 2008-10-30 | Carli Connally | Method and Apparatus for Displaying Pin Result Data |
US20090030915A1 (en) * | 2007-07-25 | 2009-01-29 | Cognos Incorporated | System and method for determining driving factor in a data cube |
US20090063517A1 (en) * | 2007-08-30 | 2009-03-05 | Microsoft Corporation | User interfaces for scoped hierarchical data sets |
US20110060753A1 (en) * | 2009-04-05 | 2011-03-10 | Guy Shaked | Methods for effective processing of time series |
US8010910B2 (en) | 2007-09-04 | 2011-08-30 | Microsoft Corporation | Breadcrumb list supplementing for hierarchical data sets |
US20130007649A1 (en) * | 2011-06-29 | 2013-01-03 | International Business Machines Corporation | Control Elements of Graphical User Interfaces |
US8676801B2 (en) | 2011-08-29 | 2014-03-18 | Sas Institute Inc. | Computer-implemented systems and methods for processing a multi-dimensional data structure |
US20140282175A1 (en) * | 2013-03-14 | 2014-09-18 | Adobe Systems Incorporated | Method and system of visually depicting hierarchical data through selective colorization |
US9607022B2 (en) | 2008-12-15 | 2017-03-28 | Tamiras Per Pte. Ltd., Llc | Automatic data store architecture detection |
JP2018022522A (en) * | 2017-10-06 | 2018-02-08 | 富士通株式会社 | Information processing program, device and method |
US10885070B2 (en) * | 2015-06-01 | 2021-01-05 | Mobile Content Management Solutions Limited | Data search method and device |
Families Citing this family (20)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080295007A1 (en) * | 2007-05-24 | 2008-11-27 | Microsoft Corporation | Data Visualization |
US8996544B2 (en) | 2012-09-28 | 2015-03-31 | Oracle International Corporation | Pruning disk blocks of a clustered table in a relational database management system |
US9430550B2 (en) | 2012-09-28 | 2016-08-30 | Oracle International Corporation | Clustering a table in a relational database management system |
US9507825B2 (en) | 2012-09-28 | 2016-11-29 | Oracle International Corporation | Techniques for partition pruning based on aggregated zone map information |
US10642837B2 (en) | 2013-03-15 | 2020-05-05 | Oracle International Corporation | Relocating derived cache during data rebalance to maintain application performance |
WO2015094315A1 (en) * | 2013-12-20 | 2015-06-25 | Hewlett-Packard Development Company, L.P. | Discarding data points in a time series |
US10489266B2 (en) * | 2013-12-20 | 2019-11-26 | Micro Focus Llc | Generating a visualization of a metric at one or multiple levels of execution of a database workload |
US10909117B2 (en) | 2013-12-20 | 2021-02-02 | Micro Focus Llc | Multiple measurements aggregated at multiple levels of execution of a workload |
US20160292895A1 (en) * | 2015-03-31 | 2016-10-06 | Rockwell Automation Technologies, Inc. | Layered map presentation for industrial data |
US10528021B2 (en) | 2015-10-30 | 2020-01-07 | Rockwell Automation Technologies, Inc. | Automated creation of industrial dashboards and widgets |
US10313281B2 (en) | 2016-01-04 | 2019-06-04 | Rockwell Automation Technologies, Inc. | Delivery of automated notifications by an industrial asset |
US10318570B2 (en) | 2016-08-18 | 2019-06-11 | Rockwell Automation Technologies, Inc. | Multimodal search input for an industrial search platform |
US10545492B2 (en) | 2016-09-26 | 2020-01-28 | Rockwell Automation Technologies, Inc. | Selective online and offline access to searchable industrial automation data |
US10401839B2 (en) | 2016-09-26 | 2019-09-03 | Rockwell Automation Technologies, Inc. | Workflow tracking and identification using an industrial monitoring system |
US10319128B2 (en) | 2016-09-26 | 2019-06-11 | Rockwell Automation Technologies, Inc. | Augmented reality presentation of an industrial environment |
US10735691B2 (en) | 2016-11-08 | 2020-08-04 | Rockwell Automation Technologies, Inc. | Virtual reality and augmented reality for industrial automation |
US10388075B2 (en) | 2016-11-08 | 2019-08-20 | Rockwell Automation Technologies, Inc. | Virtual reality and augmented reality for industrial automation |
US10866631B2 (en) | 2016-11-09 | 2020-12-15 | Rockwell Automation Technologies, Inc. | Methods, systems, apparatuses, and techniques for employing augmented reality and virtual reality |
US11086876B2 (en) | 2017-09-29 | 2021-08-10 | Oracle International Corporation | Storing derived summaries on persistent memory of a storage device |
US10445944B2 (en) | 2017-11-13 | 2019-10-15 | Rockwell Automation Technologies, Inc. | Augmented reality safety automation zone system and method |
Citations (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5767854A (en) * | 1996-09-27 | 1998-06-16 | Anwar; Mohammed S. | Multidimensional data display and manipulation system and methods for using same |
US5926820A (en) * | 1997-02-27 | 1999-07-20 | International Business Machines Corporation | Method and system for performing range max/min queries on a data cube |
US6182060B1 (en) * | 1997-04-15 | 2001-01-30 | Robert Hedgcock | Method and apparatus for storing, retrieving, and processing multi-dimensional customer-oriented data sets |
US6456999B1 (en) * | 1999-06-22 | 2002-09-24 | Microsoft Corporation | Aggregations size estimation in database services |
US6470344B1 (en) * | 1999-05-29 | 2002-10-22 | Oracle Corporation | Buffering a hierarchical index of multi-dimensional data |
US6484179B1 (en) * | 1999-10-25 | 2002-11-19 | Oracle Corporation | Storing multidimensional data in a relational database management system |
US6546135B1 (en) * | 1999-08-30 | 2003-04-08 | Mitsubishi Electric Research Laboratories, Inc | Method for representing and comparing multimedia content |
US6581068B1 (en) * | 1999-12-01 | 2003-06-17 | Cartesis, S.A. | System and method for instant consolidation, enrichment, delegation and reporting in a multidimensional database |
US6728724B1 (en) * | 1998-05-18 | 2004-04-27 | Microsoft Corporation | Method for comparative visual rendering of data |
US20040111388A1 (en) * | 2002-12-06 | 2004-06-10 | Frederic Boiscuvier | Evaluating relevance of results in a semi-structured data-base system |
US6750864B1 (en) * | 1999-11-15 | 2004-06-15 | Polyvista, Inc. | Programs and methods for the display, analysis and manipulation of multi-dimensional data implemented on a computer |
US20050066277A1 (en) * | 2003-09-19 | 2005-03-24 | Robert Leah | Methods, systems and computer program products for use of color saturation to highlight items in a tree map visualization |
US6898603B1 (en) * | 1999-10-15 | 2005-05-24 | Microsoft Corporation | Multi-dimensional data structure caching |
US7089266B2 (en) * | 2003-06-02 | 2006-08-08 | The Board Of Trustees Of The Leland Stanford Jr. University | Computer systems and methods for the query and visualization of multidimensional databases |
US7133876B2 (en) * | 2001-06-12 | 2006-11-07 | The University Of Maryland College Park | Dwarf cube architecture for reducing storage sizes of multidimensional data |
-
2005
- 2005-02-22 US US11/063,469 patent/US20060190432A1/en not_active Abandoned
-
2011
- 2011-01-18 US US13/008,413 patent/US7996378B2/en active Active
Patent Citations (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5767854A (en) * | 1996-09-27 | 1998-06-16 | Anwar; Mohammed S. | Multidimensional data display and manipulation system and methods for using same |
US5926820A (en) * | 1997-02-27 | 1999-07-20 | International Business Machines Corporation | Method and system for performing range max/min queries on a data cube |
US6182060B1 (en) * | 1997-04-15 | 2001-01-30 | Robert Hedgcock | Method and apparatus for storing, retrieving, and processing multi-dimensional customer-oriented data sets |
US6728724B1 (en) * | 1998-05-18 | 2004-04-27 | Microsoft Corporation | Method for comparative visual rendering of data |
US6470344B1 (en) * | 1999-05-29 | 2002-10-22 | Oracle Corporation | Buffering a hierarchical index of multi-dimensional data |
US6456999B1 (en) * | 1999-06-22 | 2002-09-24 | Microsoft Corporation | Aggregations size estimation in database services |
US6546135B1 (en) * | 1999-08-30 | 2003-04-08 | Mitsubishi Electric Research Laboratories, Inc | Method for representing and comparing multimedia content |
US6898603B1 (en) * | 1999-10-15 | 2005-05-24 | Microsoft Corporation | Multi-dimensional data structure caching |
US6484179B1 (en) * | 1999-10-25 | 2002-11-19 | Oracle Corporation | Storing multidimensional data in a relational database management system |
US6750864B1 (en) * | 1999-11-15 | 2004-06-15 | Polyvista, Inc. | Programs and methods for the display, analysis and manipulation of multi-dimensional data implemented on a computer |
US6581068B1 (en) * | 1999-12-01 | 2003-06-17 | Cartesis, S.A. | System and method for instant consolidation, enrichment, delegation and reporting in a multidimensional database |
US7133876B2 (en) * | 2001-06-12 | 2006-11-07 | The University Of Maryland College Park | Dwarf cube architecture for reducing storage sizes of multidimensional data |
US20040111388A1 (en) * | 2002-12-06 | 2004-06-10 | Frederic Boiscuvier | Evaluating relevance of results in a semi-structured data-base system |
US7089266B2 (en) * | 2003-06-02 | 2006-08-08 | The Board Of Trustees Of The Leland Stanford Jr. University | Computer systems and methods for the query and visualization of multidimensional databases |
US20050066277A1 (en) * | 2003-09-19 | 2005-03-24 | Robert Leah | Methods, systems and computer program products for use of color saturation to highlight items in a tree map visualization |
Cited By (21)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060107289A1 (en) * | 2004-07-28 | 2006-05-18 | Microsoft Corporation | Thumbnail generation and presentation for recorded TV programs |
US20080270848A1 (en) * | 2007-04-26 | 2008-10-30 | Carli Connally | Method and Apparatus for Displaying Pin Result Data |
US20090030915A1 (en) * | 2007-07-25 | 2009-01-29 | Cognos Incorporated | System and method for determining driving factor in a data cube |
US8234298B2 (en) * | 2007-07-25 | 2012-07-31 | International Business Machines Corporation | System and method for determining driving factor in a data cube |
TWI570575B (en) * | 2007-08-30 | 2017-02-11 | 微軟技術授權有限責任公司 | Method, computer-readable medium and system of identifying a path from a root node to a selected node in a scoped hierarchical data set and method and computer-readable medium of displaying the same |
US20090063517A1 (en) * | 2007-08-30 | 2009-03-05 | Microsoft Corporation | User interfaces for scoped hierarchical data sets |
US8010910B2 (en) | 2007-09-04 | 2011-08-30 | Microsoft Corporation | Breadcrumb list supplementing for hierarchical data sets |
US12111805B2 (en) | 2008-12-15 | 2024-10-08 | Tamiras Per Pte. Ltd., Llc | Automatic data store architecture detection |
US11593333B2 (en) | 2008-12-15 | 2023-02-28 | Tamiras Per Pte. Ltd., Llc | Automatic data store architecture detection |
US10223390B2 (en) | 2008-12-15 | 2019-03-05 | Tamiras Per Pte. Ltd., Llc | Automatic data store architecture detection |
US9607022B2 (en) | 2008-12-15 | 2017-03-28 | Tamiras Per Pte. Ltd., Llc | Automatic data store architecture detection |
US9047349B2 (en) * | 2009-04-05 | 2015-06-02 | Yanicklo Technology Limited Liability Company | Methods for effective processing of time series |
US20110060753A1 (en) * | 2009-04-05 | 2011-03-10 | Guy Shaked | Methods for effective processing of time series |
US9311353B2 (en) * | 2011-06-29 | 2016-04-12 | International Business Machines Corporation | Control elements of graphical user interfaces |
US9171035B2 (en) | 2011-06-29 | 2015-10-27 | International Business Machines Corporation | Control elements of graphical user interfaces |
US20130007649A1 (en) * | 2011-06-29 | 2013-01-03 | International Business Machines Corporation | Control Elements of Graphical User Interfaces |
US8676801B2 (en) | 2011-08-29 | 2014-03-18 | Sas Institute Inc. | Computer-implemented systems and methods for processing a multi-dimensional data structure |
US20140282175A1 (en) * | 2013-03-14 | 2014-09-18 | Adobe Systems Incorporated | Method and system of visually depicting hierarchical data through selective colorization |
US10496658B2 (en) * | 2013-03-14 | 2019-12-03 | Adobe Inc. | Method and system of visually depicting hierarchical data through selective colorization |
US10885070B2 (en) * | 2015-06-01 | 2021-01-05 | Mobile Content Management Solutions Limited | Data search method and device |
JP2018022522A (en) * | 2017-10-06 | 2018-02-08 | 富士通株式会社 | Information processing program, device and method |
Also Published As
Publication number | Publication date |
---|---|
US20110119227A1 (en) | 2011-05-19 |
US7996378B2 (en) | 2011-08-09 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US7996378B2 (en) | System and method for graphically distinguishing levels of a multidimensional database | |
US7660823B2 (en) | Computer-implemented system and method for visualizing OLAP and multidimensional data in a calendar format | |
RU2402060C2 (en) | Data access and data organisation through diagrams | |
US7730023B2 (en) | Apparatus and method for strategy map validation and visualization | |
US8190620B2 (en) | Apparatus and method for visualizing data within a decomposition graph | |
US7734576B2 (en) | Apparatus and method for relating graphical representations of data tables | |
US10846273B2 (en) | System and method for automatic inference of a cube schema from a tabular data for use in a multidimensional database environment | |
US7698349B2 (en) | Dimension member sliding in online analytical processing | |
US9547646B2 (en) | User-created members positioning for OLAP databases | |
US20090096812A1 (en) | Apparatus and method for morphing data visualizations | |
US20100318583A1 (en) | Data-visualization system and method | |
US20090327852A1 (en) | Apparatus and method for navigating a multi-dimensional database | |
US9652498B2 (en) | Processing queries using hybrid access paths | |
US9684877B2 (en) | System and method for improved consumption models for analytics | |
US20110080408A1 (en) | Dynamic generation of contextual charts based on personalized visualization preferences | |
US7523090B1 (en) | Creating data charts using enhanced SQL statements | |
Tešendić et al. | Business intelligence in the service of libraries | |
Schulz et al. | A systematic view on data descriptors for the visual analysis of tabular data | |
Gür et al. | GeoSemOLAP: Geospatial OLAP on the Semantic Web made easy | |
US11734309B2 (en) | Nested group hierarchies for analytics applications | |
US7308457B1 (en) | Method and apparatus for providing customized filters to restrict datasets retrieved from a database | |
EP1494139A2 (en) | Drill-through queries from data mining model content | |
US20090199158A1 (en) | Apparatus and method for building a component to display documents relevant to the content of a website | |
US20150363711A1 (en) | Device for rapid operational visibility and analytics automation | |
US10346421B1 (en) | Data profiling of large datasets |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: SAS INSTITUTE INC., NORTH CAROLINA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:WANG, HUIFANG;CLOVER, LINA;CRABTREE, WALDEN B.;AND OTHERS;REEL/FRAME:016331/0117 Effective date: 20050217 |
|
STCB | Information on status: application discontinuation |
Free format text: EXPRESSLY ABANDONED -- DURING EXAMINATION |