Patent Abstract:
Creating and maintaining cell data records in a multidimensional database is disclosed. The systems and methods of the invention define an efficient mechanism to specify a cell&#39;s location within the multidimensional database where there are hierarchies of levels within a dimension. The format used lends itself well to indexing, and also to creating aggregations of the cell data.

Full Description:
RELATED APPLICATIONS 
     This application is related to the following applications, all of which are filed on the same day and assigned to the same assignee as the present application: 
     “Aggregation Design in Database Services”—Ser. No. 09/338,212, filed on Jun. 22, 1999 issued as U.S. Pat. No. 6,366,905 on Apr. 2, 2002, 
     “Aggregation Size Estimation in Relational and OLAP Databases”—Ser. No. 09/338,390 filed on Jun. 22, 1999, 
     “Aggregation Performance Estimation in Relational and OLAP Databases”—Ser. No. 09/337,751 filed on Jun. 22, 1999 issued as U.S. Pat. No. 6,374,234 on Apr. 16, 2002, 
     “Usage Based Aggregation Optimization”—Ser. No. 09/337,226 filed Jun. 22, 1999, and 
     “Record for Multidimensional Database With Flexible Pathing”—Ser. No. 09/338,207 filed on Jun. 22, 1999. 
    
    
     COPYRIGHT NOTICE AND PERMISSION 
     A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever. The following notice shall apply to this document: Copyright © 1999, Microsoft, Inc. 
     FIELD 
     The present invention pertains generally to computer-implemented databases, and more particularly to storing records in such databases. 
     BACKGROUND 
     Online analytical processing (OLAP) is a key part of most data warehouse and business analysis systems. OLAP services provide for fast analysis of multidimensional information. For this purpose, OLAP services provide for multidimensional access and navigation of data in an intuitive and natural way, providing a global view of data that can be drilled down into particular data of interest. Speed and response time are important attributes of OLAP services that allow users to browse and analyze data online in an efficient manner. Further, OLAP services typically provide analytical tools to rank, aggregate, and calculate lead and lag indicators for the data under analysis. 
     In this context, a dimension is a structural attribute of a cube that is a list of members of a similar type in the user&#39;s perception of the data. Typically, there is a hierarchy associated with the dimension. For example, a time dimension can consist of days, weeks, months, and years, while a geography dimension can consist of cities, states/provinces, and countries. Dimension members act as indices for identifying a particular cell or range of cells within a multidimensional array. Each cell contains a value, also referred to as a measurement. 
     One issue regarding the design of multidimensional databases is how to represent the cells in the multidimensional space. One potential design choice is to represent the multidimensional space as an array of cells, with the size of the array determined by the multiplication of the number of points in each dimension. A significant problem with this approach is that the size of the database grows exponentially as the number of dimensions and the size of each dimension increases. This leads to a rapid depletion of the physical resources such as persistent storage and RAM required to implement the database. This phenomenon is known as data explosion for multidimensional databases. 
     In addition, much of the space is wasted in the above-mentioned approach. Data in multidimensional databases tends to be sparse, that is, not every cell is expected to have a value associated with it. For example, consider a Store dimension having a hierarchy of Country, State, and City specifying the location of a store, and a Product dimension having a product identification and a product count measure. No store in the in the database will be expected to stock every possible product, and in fact any one store may only stock 20% of the available products. In this situation, most of the cells in the multidimensional space would contain no data, thus wasting much of the space allocated to the database. 
     A second issue relates to locating cells in the multidimensional space. It is desirable to be able to locate cells quickly in order to provide acceptable system throughput. Representing the cells as a multidimensional array provides for rapid access to the cells, but has the data explosion problem mentioned above. 
     A third issue relates to the capability to perform aggregations on the multidimensional data. Databases are commonly queried for aggregations (e.g. summaries, minimums, maximums, counts etc.) of detail data rather than individual data items. For example, a user might want to know sales data for a given period of time without regard to geographical distinctions. These types of queries are efficiently answered through aggregations. Aggregations are precomputed summaries of selected detail data that allow an OLAP system or a relational database to respond quickly to queries by avoiding collecting and aggregating detailed data during query execution. Without aggregations, the system would need to scan all of the rows containing the detailed data to answer these queries, resulting in potentially substantial processing delays. With aggregations, the system computes and materializes aggregations ahead of time so that when the query is submitted to the system, the appropriate summary already exists and can be sent to the user much more quickly. Calculating these aggregations, however, can be costly, both in terms of processing time and in terms of disk space consumed. 
     Thus there is a need for a system that stores cell data for a multidimensional database in an efficient manner. There is a need for such a system that provides the ability to locate cells rapidly and efficiently. Furthermore, there is a need for such a system that is able to perform aggregations in an efficient manner. 
     SUMMARY 
     The above-mentioned shortcomings, disadvantages and problems are addressed by the present invention, which will be understood by reading and studying the following specification. 
     The systems and methods described herein create and maintain cell data records in an OLAP database system. One aspect of the system is that cell data records are created that contain a system path. The system path is comprised of one or more dimension paths that define the location of a cell in a multidimensional database. The format used for the dimension paths provides an efficient mechanism for locating the cell, and in addition, can be indexed easily to allow rapid location of cell data. 
     A further aspect of the system is that the format of the system path provides an efficient mechanism for creating aggregations. Those dimension levels that are to be aggregated have their corresponding member index set to a null value in the dimension path of each record. The records are then scanned for a match to a system path representing the aggregation. Those that match have their measure data included in the aggregation. 
     The present invention describes systems, clients, servers, methods, and computer-readable media of varying scope. In addition to the aspects and advantages of the present invention described in this summary, further aspects and advantages of the invention will become apparent by reference to the drawings and by reading the detailed description that follows. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 shows a diagram of the hardware and operating environment in conjunction with which embodiments of the invention may be practiced; 
     FIGS. 2A-2C are diagrams illustrating an exemplary dimension hierarchy within a multidimensional database; 
     FIG. 3 is a diagram illustrating a record structure for a cell data record according to an embodiment of the invention; 
     FIG. 4 is a system level overview of various embodiments of the invention; 
     FIG. 5 is a flowchart illustrating a process for creating a cell data record according to an embodiment of the invention; and 
     FIG. 6 is a flowchart illustrating a process for calculating an aggregation according to an embodiment of the invention. 
    
    
     DETAILED DESCRIPTION 
     In the following detailed description of exemplary embodiments of the invention, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific exemplary embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that other embodiments may be utilized and that logical, mechanical, electrical and other changes may be made without departing from the spirit or scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims. 
     The detailed description is divided into five sections. In the first section, the hardware and the operating environment in conjunction with which embodiments of the invention may be practiced are described. In the second section, an exemplary cube for an OLAP system is described. In the third section, a system level overview of an exemplary embodiment of the invention is presented. In the fourth section, methods of an exemplary embodiment of the invention are provided. Finally, in the fifth section, a conclusion of the detailed description is provided. 
     Hardware and Operating Environment 
     FIG. 1 is a diagram of the hardware and operating environment in conjunction with which embodiments of the invention may be practiced. The description of FIG. 1 is intended to provide a brief, general description of suitable computer hardware and a suitable computing environment in conjunction with which the invention may be implemented. Although not required, the invention is described in the general context of computer-executable instructions, such as program modules, being executed by a computer, such as a personal computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. 
     Moreover, those skilled in the art will appreciate that the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCS, minicomputers, mainframe computers, and the like. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. 
     The exemplary hardware and operating environment of FIG. 1 for implementing the invention includes a general purpose computing device in the form of a computer  20 , including a processing unit  21 , a system memory  22 , and a system bus  23  that operatively couples various system components including the system memory to the processing unit  21 . There may be only one or there may be more than one processing unit  21 , such that the processor of computer  20  comprises a single central-processing unit (CPU), or a plurality of processing units, commonly referred to as a parallel processing environment. The computer  20  may be a conventional computer, a distributed computer, or any other type of computer; the invention is not so limited. 
     The system bus  23  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The system memory may also be referred to as simply the memory, and includes read only memory (ROM)  24  and random access memory (RAM)  25 . A basic input/output system (BIOS)  26 , containing the basic routines that help to transfer information between elements within the computer  20 , such as during start-up, is stored in ROM  24 . The computer  20  further includes a hard disk drive  27  for reading from and writing to a hard disk, not shown, a magnetic disk drive  28  for reading from or writing to a removable magnetic disk  29 , and an optical disk drive  30  for reading from or writing to a removable optical disk  31  such as a CD ROM or other optical media. 
     The hard disk drive  27 , magnetic disk drive  28 , and optical disk drive  30  are connected to the system bus  23  by a hard disk drive interface  32 , a magnetic disk drive interface  33 , and an optical disk drive interface  34 , respectively. The drives and their associated computer-readable media provide nonvolatile storage of computer-readable instructions, data structures, program modules and other data for the computer  20 . It should be appreciated by those skilled in the art that any type of computer-readable media which can store data that is accessible by a computer, such as magnetic cassettes, flash memory cards, digital video disks, Bernoulli cartridges, random access memories (RAMs), read only memories (ROMs), and the like, may be used in the exemplary operating environment. 
     A number of program modules may be stored on the hard disk, magnetic disk  29 , optical disk  31 , ROM  24 , or RAM  25 , including an operating system  35 , one or more application programs  36 , other program modules  37 , and program data  38 . A user may enter commands and information into the personal computer  20  through input devices such as a keyboard  40  and pointing device  42 . Other input devices (not shown) may include a microphone, joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  21  through a serial port interface  46  that is coupled to the system bus, but may be connected by other interfaces, such as a parallel port, game port, or a universal serial bus (USB). A monitor  47  or other type of display device is also connected to the system bus  23  via an interface, such as a video adapter  48 . In addition to the monitor, computers typically include other peripheral output devices (not shown), such as speakers and printers. 
     The computer  20  may operate in a networked environment using logical connections to one or more remote computers, such as remote computer  49 . These logical connections are achieved by a communication device coupled to or a part of the computer  20 ; the invention is not limited to a particular type of communications device. The remote computer  49  may be another computer, a server, a router, a network PC, a client, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  20 , although only a memory storage device  50  has been illustrated in FIG.  1 . The logical connections depicted in FIG. 1 include a local-area network (LAN)  51  and a wide-area network (WAN)  52 . Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
     When used in a LAN-networking environment, the computer  20  is connected to the local network  51  through a network interface or adapter  53 , which is one type of communications device. When used in a WAN-networking environment, the computer  20  typically includes a modem  54 , a type of communications device, or any other type of communications device for establishing communications over the wide area network  52 , such as the Internet. The modem  54 , which may be internal or external, is connected to the system bus  23  via the serial port interface  46 . In a networked environment, program modules depicted relative to the personal computer  20 , or portions thereof, may be stored in the remote memory storage device. It is appreciated that the network connections shown are exemplary and other means of and communications devices for establishing a communications link between the computers may be used. 
     The hardware and operating environment in conjunction with which embodiments of the invention may be practiced has been described. The computer in conjunction with which embodiments of the invention may be practiced may be a conventional computer, a distributed computer, or any other type of computer; the invention is not so limited. Such a computer typically includes one or more processing units as its processor, and a computer-readable medium such as a memory. The computer may also include a communications device such as a network adapter or a modem, so that it is able to communicatively couple other computers. 
     Exemplary Cube and Dimension 
     In the detailed description that follows, reference will be made to a small, three-dimensional OLAP cube. This cube has a Customers dimension with four levels: All, State, City and Customer. The second dimension, a Products dimension, has three levels: All, Category and Product. The final dimension is a time dimension with three hierarchy levels: year, quarter, and month. In addition, the cube has two measures, Purchases and Units. This cube is presented to provide a reference example of how the systems and methods of the invention operate. It will be appreciated that the OLAP cubes maintained by various embodiments of the invention can have more or fewer dimensions than in this example, and that the OLAP cube can have more or fewer hierarchy levels than in this example. 
     A graphical representation of the dimensions in the above-described cube is presented in FIGS. 2A-2C. A dimension is represented as a tree, referred to as a dimension tree. Leaf nodes in the tree correspond to the most detailed data in the dimension, while the inner branch nodes correspond to more aggregated data. The closer the node is to the root node, the more aggregated the data, with the root node representing the most aggregated, least detailed data in the dimension. 
     The Customer dimension is represented in FIG.  2 A. In this exemplary representation, the State level has three members: Maine, Oregon and Washington. The Cities level has four members: Portland (Me.), Portland (Oreg.), Redmond and Seattle. It should be noted although a member labeled Portland appears twice, each member is a distinct reference because it appears under a different State member in the hierarchy. The Customer level has four members: Alexander, Amir, Mosha and Sasha. The Products dimension is represented in FIG.  2 B. In the exemplary representation, the Category level has three members: Food, Drink, and Non-Consumable. The Product level has one member, Milk. 
     The Time dimension is represented in FIG.  2 C. In the exemplary representation, The Year level has three members: 1997, 1998, and 1999. The Quarter level has four members: Q 1 -Q 4 . The Month level has no members, indicating that no monthly data is available. In this case, the most detailed data available is at the Quarter level. 
     In order to uniquely identify a particular member, each of the members from the root node to the leaf node for the member must be specified. For example, in order to refer to the customer Amir in the Customers dimension shown in FIG. 2A the following sequence of members is specified: {All Customers}. {WA}. {Redmond}. {Amir}. Similarly, to refer to Quarter  2  in the Time dimension shown in FIG. 2C, the members specified are: {1998}.{Q 2 }. 
     Those of skill in the art will appreciate that the members shown in FIGS. 2A-2C represent an exemplary cube and that no embodiment of the invention is limited to a particular number or type of dimensions or dimension members. 
     In the above example, strings representing member names are used to designate a particular member of a dimension. In an embodiment of the invention, the strings above are replaced by numbers associated with each member in a dimension. It is desirable to represent the members using numbers as it is more efficient to represent objects using numbers rather than strings. In this embodiment, a path from the root node to a branch node is represented by the member number at each level of the dimension that is traversed to reach the leaf node. The number assigned to each member must be unique among the members having a common parent, in other words, a unique number must be assigned to each of the siblings of a parent. In one embodiment of the invention, the root node is assigned the number  1  while branch and leaf nodes are assigned a number representing their order among their siblings. However the invention is not limited to any particular numbering scheme for the node, all that is required is that the number be unique among the nodes having a common parent. 
     Thus, each member in a dimension can be represented by an array of numbers defining the path to the member. This array is the dimension path. The number of elements in the array is the number of levels in the dimension, and the position in the array reflects the hierarchy of levels. For example referring to FIG. 2A, the representation for member Amir in the Customers dimension is the dimension path {1-48-2-2}. This represents the path comprising the root node All Customers (1), the WA member at the state level (WA is the 48 th  state alphabetically), the Redmond member at the city level (Redmond is the second city at that level under WA), and the member Amir at the customer level (Amir is the second customer under Redmond). Note that each level must be represented by a number in the array, if the member is not at a leaf node, the number 0 is used in one embodiment of the invention to represent the positions for the levels below the member. Thus the dimension path array for the member Portland, Oreg. in the Customer dimension is {1-38-1-0}. 
     Not all dimensions have a single root member. For example, consider the Time dimension of the exemplary cube. There is no single “all time” member at the top-most level in this dimension, rather the Time dimension contains three members, each specifying a particular year. In this case, one embodiment of the invention assigns an index number to each members in the top-most level based on a natural order of the members. This natural order can be based on a numeric order, an alphabetic order, or the temporal order in which the members were created. For instance, in FIG. 2C, the dimension path for Q 3  in the year 1998 is {2-3-0} (1998 is the second year at the top-most level, Q 3  is the third member under 1998, and there are no month members). 
     Each data cell in a multidimensional database is uniquely identified by specifying a coordinate on each dimension. In one embodiment of the invention, a cell is identified by specifying a dimension path for each dimension in a cube in the multidimensional database. The collection of dimension paths comprising the coordinates for the cell are concatenated and stored in an array referred to as the system path. In an embodiment of the invention, the order of dimension paths in the system path is dependent on the internal order of the dimensions in the cube, as determined by the metadata defining the cube. However, the invention is not limited to a particular ordering scheme and other ordering schemes are possible and within the scope of the invention. For example, the order of dimension paths could be determined alphabetically by the name of the dimension. 
     To illustrate the system path described above, consider the cell associated with the customer Amir for All Products in Quarter  4  of 1998. The string representation for the cell path is: ({Customers}. {All 13 Customers}. {WA}. {Redmond}. {Amir}, {Products}. {All Products}, {Time}.{1998}.{Q 4 }). The corresponding system path is: {1-48-2-2}-{1-0-0}- { 2-4-0 }. 
     Each cell in a multidimensional database has one or more measures associated with it. In the exemplary cube, two measures are defined, Purchases and Units, where Purchases is the dollar amount of a particular purchase, and Units is the number of units purchased. FIG. 3 illustrates a data structure for a cell record  300  according to one embodiment of the invention. Cell record  300  contains a system path  305  and one or more measures  310 . As described above, system path  305  comprises one or more dimension paths  315 . The order of measures  310  in record  300  can be determined by the order of the measures in the metadata defining the cube, the temporal order in which the measure were defined, or an alphabetic order. The invention is not limited to any particular ordering mechanism. 
     This section of the detailed description has described a representation of cells in a multidimensional database, and a data structure for storing a cell record. In the sections that follow, systems and methods for creating and manipulating the cell data will be described. 
     System Level Overview 
     A system level overview of the operation of an exemplary embodiment of the invention is described by reference to FIG.  4 . The concepts of the invention are described as operating in a multiprocessing, multithreaded virtual memory operating environment on a computer, such as computer  20  in FIG.  1 . The operating environment includes an OLAP client  402 , OLAP server  410 , local data store  414 , and fact data store  420 , all of which operate on the cell records for cubes, including the records and cube described in the previous section. 
     OLAP client  402  is an application program that requires the services of an OLAP system. OLAP client  402  can be any type of application that interacts with the OLAP system, for example, a data mining application, a data warehousing application, a reporting application etc. OLAP client  402  typically interacts with OLAP server  260  by issuing OLAP queries. These queries are parsed, as is known in the art, into a request for data from a cell or range of cells, and the request is passed to the OLAP server  410 . 
     OLAP server  410  receives queries and controls the processing of queries. In one embodiment of the invention, the server maintains a local store  414  that contains the cell data used to answer the queries. In one embodiment of the invention, the OLAP server  410  is a version of the SQL Server OLAP product from Microsoft Corporation. 
     The local store  414  contains records describing the cells that are present in a multidimensional database, with one record used for each cell that actually has measurement data present (i.e. no records exist for those cells having no measurement data). The general format of these records is described above with reference to FIG.  3 . In one embodiment of the invention, local store  414  is a relational database, such as SQL Server. In this embodiment, records are stored in a relational table. This table can be indexed based on the dimensional paths of the record to allow rapid access to cell measurement data contained in the record. The indexing can be performed using hash indexing or AVL tree indexing as is known in the art. 
     OLAP server  410  populates local store  414  by reading data from fact data store  420 . Fact data store  420  is also a relational database system. In one embodiment of the invention, the system used is the SQL Server Database from Microsoft Corporation. In alternative embodiments of the invention, database systems such as Oracle, Informix or Sybase can be used. The invention is not limited to any particular type of relational database system. 
     OLAP server  410  reads the fact data (also known as detail data) from fact data store  420  at predetermined times, and converts the fact data into cell data records for populating local data store  414 . In one embodiment of the invention, the fact data is read once during a  24  hour period, typically during a time when the fact data store is not busy responding to user queries. In an alternative embodiment of the invention, the fact data is read and converted when a system administrator issues a command to the OLAP server  410  to do so. Updates to the local data store  414  can be incremental, or they can result in a complete refresh of the data. Incremental updates are desirable, because only the data that has changed in fact data store  420  need be converted and added to local data store  414 . However, if the structure of the data in either fact data store  420  or local data store  414  changes, then a complete refresh is required. The frequency of updates to the local store  414  will generally be determined by user requirements as to how current (or accurate) the cell data must be, and the volume of data that must be updated. 
     In one embodiment of the invention, the OLAP server  410  maintains a cache  412  of cell records. In this embodiment, the cache maintains cell data records that have been recently requested, or those cell data records that are frequently requested. Maintaining cell record data in a cache is desirable, because it provides quicker responses to queries that can be satisfied by records appearing in the cache. 
     Methods of an Exemplary Embodiment of the Invention 
     In the previous section, a system level overview of the operation of an exemplary embodiment of the invention was described. In this section, the particular methods of the invention performed by an operating environment executing an exemplary embodiment are described by reference to a series of flowcharts shown in FIGS. 5 and 6. The methods to be performed by the operating environment constitute computer programs made up of computer-executable instructions. Describing the methods by reference to a flowchart enables one skilled in the art to develop such programs including such instructions to carry out the methods on suitable computers (the processor of the computer executing the instructions from computer-readable media). The methods illustrated in FIGS. 5 and 6 are inclusive of the acts required to be taken by an operating environment executing an exemplary embodiment of the invention. 
     A method for creating a cell data record is illustrated in FIG.  5 . The method begins when a program executing the method, such as OLAP server  420 , discovers that a new cell is required, and receives a value to be used for the measure of the new cell (block  502 ). Typically the new cell will be required because OLAP Server  410  has discovered that a new row has been added to a detail table in a fact data store  420  (FIG. 4) since the last update of the local data store  414 . 
     Next, the program determines the dimension path for each dimension in the cube to which the cell belongs (block  504 ). As discussed above, cells are located by specifying members in each dimension of the cube. The members will reside at a particular level of a dimension tree formed by the levels of the dimension and the members at each level. In one embodiment of the invention, the dimension path is an array of ordinal numbers, one for each level in the dimension. The position of each ordinal number in the array is determined by the position of the level in the dimension hierarchy. The ordinal number at a position is determined by an ordering of the members at the particular level represented by the position that have a common parent. If the new cell is not a leaf node, then a value of 0 is used in the dimension path to represent each of the levels below the new cell. 
     The program then proceeds to concatenate the dimension paths formed at block  504  into a system path (block  506 ) for the new cell record. In one embodiment of the invention, the ordering of the dimension paths in the system path is determined by order the dimensions are defined in the cube metadata. However, the invention is not so limited, and in alternative embodiments, the ordering can be determined by temporal order or alphabetic order. 
     Next, the measure data is copied into an appropriate field in the cell record (block  508 ). The cell record contains a field for each measure present in the cube. The ordering of measures within a record is also determined by the metadata defining the cube. 
     Finally, the cell record is stored in the local data store (block  510 ). In one embodiment of the invention, the cell record is stored as a row of a relational database. The row can be indexed by the system path, allowing subsequent queries requiring the cell&#39;s measures to find the cell quickly. 
     FIG. 6 shows a method for creating an aggregation of cell data records created using the method described above in reference to FIG.  5 . Table 1 below provides an exemplary set of data that will be used to demonstrate the results of executing the method. The system paths shown in Table 1are created using the dimensions of the multidimensional database illustrated in FIGS. 2A-2C. Table 1 contains four records created as described above in reference to FIG.  5 . The Member column shows the name of the member in the customer dimension, the System Path column shows the system path corresponding to the cells location in the customer, product and time dimensions (in that order). The third column shows the Product Sales measure for the cell referenced by the system path. The four records represent sales to four customers, Sasha, Alexander, Amir and Mosha for all products in the fourth quarter of 1998. 
     
       
         
               
               
               
               
             
           
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Member 
                 System Path 
                 Product Sales 
               
               
                   
                   
               
             
             
               
                   
                 Alexander 
                 {1-48-2-1}-{1-0-0}-{2-4-0} 
                 $3,000.00 
               
               
                   
                 Amir 
                 {1-48-2-2}-{1-0-0}-{2-4-0} 
                 $2,500.00 
               
               
                   
                 Mosha 
                 {1-48-2-3}-{1-0-0}-{2-4-0} 
                 $5,000.00 
               
               
                   
                 Sasha 
                 {1-20-1-1}-{1-0-0}-{2-4-0} 
                 $8,000.00 
               
               
                   
                   
               
             
          
         
       
     
     A program executing the method, such as OLAP server  410 , begins by identifying a dimension and level to aggregate (block  602 ). Typically this will be in response to a request to create an aggregation. The request may come from a system administrator, or it can be a system generated request. As an example, consider a request to aggregate all of the customer sales in Redmond, Wash. in the fourth quarter of 1998. 
     In response to the request, the system then creates a system path for the aggregation record using the dimensions and levels specified in the request (block  604 ). For the example case, the aggregation system path is {1-48-2-0}-{1-0-0}-{2-4-0}. 
     Next, the system scans the local data store containing the cell data records, and “nullifies” (sets to null) the level numbers in the dimension paths for those levels at or below the levels are to be aggregated (block  606 ). Table 2 shows the results of nullifying the appropriate level numbers. 
     
       
         
               
               
               
               
             
           
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                 Member 
                 System Path 
                 Product Sales 
               
               
                   
                   
               
             
             
               
                   
                 Alexander 
                 {1-48-2-0}-{1-0-0}-{2-4-0} 
                 $3,000.00 
               
               
                   
                 Amir 
                 {1-48-2-0}-{1-0-0}-{2-4-0} 
                 $2,500.00 
               
               
                   
                 Mosha 
                 {1-48-2-0}-{1-0-0}-{2-4-0} 
                 $5,000.00 
               
               
                   
                 Sasha 
                 {1-20-1-0}-{1-0-0}-{2-4-0} 
                 $8,000.00 
               
               
                   
                   
               
             
          
         
       
     
     Next, a program executing the method sums the desired measure for all cell records where the system path of the cell record matches the system path of the aggregation record (block  608 ). In the example shown above, the aggregation record is: {1-48-2-0}-{1-0-0}-{2-4-0}{$10500.00} 
     This aggregation reflects the fact that system paths for Customer members Alexander, Amir and Mosha matched the aggregation system path. 
     Finally, the system stores the aggregation record (block  610 ). In one embodiment of the invention, the aggregation record is stored in a cache maintained by the OLAP server. This is desirable, because it allows the aggregation record to be located quickly, thereby increasing system throughput. 
     In one embodiment of the invention, the nullification of dimension path elements is accomplished using temporary buffers. The source records are kept in their original, unconverted state and the nullification and summation operations described above are performed on copies of the source records maintained in the temporary buffers. This has the advantage that there is no need to restore values in the source records after the aggregation has been performed, the system need only delete the temporary buffers. 
     Conclusion 
     The creation and maintenance of a cell data record for a multidimensional database has been described. The systems and methods of the invention provide advantages not found in previous systems. For example, only those cells that actually contain measure data have records allocated to them. This provides for the efficient storage of cell data, even when the cell data is sparse. In addition, the format used to specify the dimensions and levels used to locate the cell can be easily indexed to allow the cell data to be located quickly. Furthermore, the format of the record allows cell data to be easily aggregated. 
     Although specific embodiments have been illustrated and described herein, it will be appreciated by those of ordinary skill in the art that any arrangement which is calculated to achieve the same purpose may be substituted for the specific embodiments shown. This application is intended to cover any adaptations or variations of the present invention. For example, those of ordinary skill within the art will appreciate that while the systems and methods have been described in the context of a multidimensional database system, the systems and method of the invention can be applied to other data that is hierarchical in nature. The terminology used in this application with respect creating and maintaining cell records is meant to include all of these environments. Therefore, it is manifestly intended that this invention be limited only by the following claims and equivalents thereof.

Technology Classification (CPC): 8