Abstract:
A computer-based method for extracting multi-dimensional data from a spreadsheet is disclosed. The method includes a spreadsheet application that has a language for spreadsheet expressions describing calculation relationships among data entities in the spreadsheet application. The method also includes steps for providing a multi-dimensional data storage that has a n-cube (or “cube”) data definition language; providing a spreadsheet in the spreadsheet application that contains a plurality of spreadsheet expressions; parsing the plurality of spreadsheet expressions; and transforming the set of spreadsheet expressions into a set of cube expressions for defining a set of cube entities, which include dimensions. The cube expressions conform to the cube data definition language, and each spreadsheet expression corresponds to a cube expression. The calculation relationships among data entities in the spreadsheet application are transformed into corresponding calculation relationships among the cube entities. The method further includes causing the set of spreadsheet expressions to create the corresponding calculation relationships within the cube.

Description:
TECHNICAL FIELD  
         [0001]    This invention relates to computer information systems, and more particularly to spreadsheet applications and multi-dimensional databases.  
         BACKGROUND  
         [0002]    Spreadsheet applications display data in sheets having rows and columns. Spreadsheet applications are a useful tool for viewing and editing tabular data, i.e. data that fits into rows and columns. For example, as of the writing of this application, the most popular spreadsheet application on the market is Microsoft® Excel (“Excel”), sold by Microsoft Corporation of Redmond, Wash., USA. Excel is one of the top-selling pieces of software of any description. Many computer users are familiar with its tools and techniques.  
           [0003]    Many types of information that have simple repeated data structures can be represented in a table, and therefore in a spreadsheet application. For instance, spreadsheet columns may represent the repeated elements of the data structure (sometimes known as “fields”) while rows represent each instance of the information structure, or “record.” Other orientations are possible, too. For example, a carpenter might keep his lumber inventory in a spreadsheet using columns for linear measures such as height, width, and length. Additional information might include the grade of the lumber, where grade is chosen from a short list of possible values, plus an integer value for quantity on hand. The first row would label each column, while subsequent rows would represent the inventory of each group of lumber. For simple inventory purposes, this might be sufficient to the carpenter&#39;s needs.  
           [0004]    However, some information is more usefully represented in multi-dimensional form. Suppose the carpenter also wanted information about the wood itself, categorizing softwoods such as balsa and pine as well as hardwoods like maple and oak. This categorization is known as a dimension. A dimension may contain, as in this example, hierarchies. This particular hierarchy works as follows: at a first level, it can consider softwood versus hardwood; at a second level, it can consider the particular tree; and, there could be subsequent levels, such as dividing pine into white pine and yellow pine. Information that is dimensional in this way is unwieldy for a spreadsheet to store. By contrast, multi-dimensional databases have been designed specifically for this purpose.  
           [0005]    Multi-dimensional databases allow a user to view dimensional data at each of its levels and across multiple dimensions. In the process, there is usually a numeric “measure” dimension being aggregated; the type of wood in the lumber inventory, for example, is of little use for inventory purposes unless it can be compared to the quantity on hand. Thus, a multi-dimensional database might have a dimension for wood type and a measure for quantity. This is why the databases are called multi-dimensional: multiple independent dimensions may be defined over the data. A collection of n dimensions and measures (as data structures) together with the information inside the structures is called a “n-cube,” or “cube” for short.  
           [0006]    Often, a cube includes a time-based dimension. Time can be hierarchically represented using levels that contain, for instance, year, quarter, and month. Suppose the carpenter wanted to track the date each piece of wood was milled, so that particularly well-aged pieces could be set aside for fine cabinetry. A multi-dimensional database could support a view of his data showing the quantity of his hardwoods grouped by year; another view into the same data set might show only maple, and aggregate the quantity by month. These sorts of view are “slices” of the cube. A slice is defined by holding a member (or set of members) constant and letting the rest of the cube&#39;s dimensions and members vary.  
           [0007]    The ability to choose slices for various perspectives on data is one reason multi-dimensional databases can process information in useful ways not available to tabular-data engines. However, the software available for accessing multi-dimensional databases has, to date, not achieved the widespread use that spreadsheet applications have achieved.  
           [0008]    An example of a multi-dimensional database product is Microsoft® SQL Server™ 2000 Analysis Services (“Analysis Services”), also a product of Microsoft Corporation of Redmond, Wash., USA. The syntax for definition and manipulation of multi-dimensional objects and data in Analysis Services is known as “MDX,” an acronym for Multidimensional Expressions. Other vendors such as Oracle Corp., of Redwood Shores, Calif., USA, sell comparable products.  
           [0009]    Following are some additional concepts and terminology for multi-dimensional databases.  
           [0010]    A multi-dimensional database usually has a data-definition language, or DDL, which includes commands for configuring data structures in the database. For a multi-dimensional database, for instance, the DDL can be used to create, delete, and modify cubes and cube elements. MDX can act as a DDL for Analysis Services.  
           [0011]    A member is an element within a dimension. A member belongs to exactly one dimension; it also belongs to exactly one of the dimension&#39;s levels; and by the nature of hierarchies, any member below the first level belongs to one member on each level above it in the hierarchy. A member can be written in the following notation if its name is unique among the members of its dimension:  
           [0012]    [Dimension name].[Member name] 
           [0013]    In general, a member can be written as:  
           [0014]    [Dimension name].[Hierarchy name].[Level name].[Member name] 
           [0015]    Some multi-dimensional databases, for example Analysis Services, support calculated members, defined using calculation rules. The calculation rules may draw upon values from multiple dimensions. For example, in the lumber inventory cube, suppose the measures include “quantity on hand” and “quantity committed to projects.” A calculated member might be “quantity available,” defined as the quantity on hand less the quantity committed to projects. MDX includes features for defining a calculated member&#39;s formula.  
           [0016]    By holding a member (or set of members) constant and letting the rest of the cube&#39;s dimensions and members vary, one can look at a “slice” of the cube data. A slice will usually contain a series of measure values. A slice is a view of the cube that contains one member for each background dimension plus all selected members for all row and column dimensions. A “tuple” is a collection of members. The notation for tuples is a comma-separated list, enclosed in parentheses. A tuple defines a slice; conversely, if you list the members held constant by a slice, a slice defines a tuple. Thus, the two are closely related. “Tuple” usually refers to the expression, while “slice” usually refers to the associated data.  
           [0017]    A “cube cell” as we shall use the term is a slice that has at least one member specified for every available dimension (except the measures—the cube cell has a value for each measure). An “intersect” of a cube has at least one member specified for every available dimension, and also has exactly one specified member of a measure. Thus, an intersect is a cube cell that has one measure member specified.  
           [0018]    A “parent cell” is a cell that, in at least one of its dimensions, is not at the lowest possible level. That is, one of its members has children beneath it in at least one hierarchy. A “calculated cell” is a cell whose value is based on a formula and derives its measure values, via the formula, from the measures of others. Thus, a calculated cell is not unlike a formula cell in a spreadsheet. The formula may cause the values of a calculated cell to depend on several other cells or slices.  
         SUMMARY  
         [0019]    In general, in one aspect, the invention is a computer-based method for extracting multi-dimensional data from a spreadsheet. The method includes providing a multi-dimensional data storage that has a cube data definition language, and providing the spreadsheet in a spreadsheet application that has a language for spreadsheet expressions. The spreadsheet expressions describe calculation relationships among data entities in the spreadsheet application. The spreadsheet contains a plurality of spreadsheet expressions that the method parses. The method includes transforming the set of spreadsheet expressions into a set of cube expressions for defining a set of cube entities, which include dimensions. The cube expressions conform to the cube data definition language. The transforming is such that each spreadsheet expression corresponds to a cube expression, and the calculation relationships among data entities in the spreadsheet application are transformed into corresponding calculation relationships among the cube entities. The method further includes causing the set of spreadsheet expressions to create the corresponding calculation relationships within the multi-dimensional data storage.  
           [0020]    Preferred embodiments include one or more of the following features. The parsing step includes parsing the plurality of spreadsheet calculation expressions into a set of spreadsheet fact expressions and a set of spreadsheet derivative expressions, while the transforming step includes transforming the set of spreadsheet fact expressions into a set of cube fact expressions defining a set of cube fact entities. The cube fact expressions are expressions within the cube data definition language, such that each spreadsheet fact expression corresponds to a cube fact expression. The transforming step further includes transforming the set of spreadsheet derivative expressions into a set of cube derivative expressions defining a set of cube derivative entities, where the cube derivative expressions are expressions within the cube data definition language. The submitting step includes submitting the set of spreadsheet fact expressions to the multi-dimensional data storage to create the set of cube fact entities, and submitting the set of spreadsheet derivative expressions to the multi-dimensional data storage to create the set of cube derivative entities.  
           [0021]    Also in preferred embodiments, the method includes a step for moving data from the spreadsheet fact expressions into the corresponding cube fact entities, using the correspondence defined during the step of transforming the set of spreadsheet fact expressions. Also, the multi-dimensional data storage includes a set of cube fact entities, and the parsing step includes parsing the plurality of spreadsheet calculation expressions into a set of spreadsheet fact expressions and a set of spreadsheet derivative expressions, where the set of spreadsheet derivative expressions is possibly empty. Furthermore, the transforming step includes transforming the set of spreadsheet derivative expressions into a set of cube derivative expressions defining a set of cube derivative entities, where the cube derivative expressions are expressions within the cube data definition language. The submitting includes submitting the set of spreadsheet derivative expressions to the multi-dimensional data storage to create the set of cube derivative entities.  
           [0022]    Still more features include the following. The transforming step consolidating the set of cube expressions into a consolidated set of cube expressions having equivalent collective scope, equivalent calculation behavior, and fewer expressions than the set of cube expressions contained before the consolidating. An interactive dialog wizard provides at least part of the user&#39;s interaction with the method. The method is implemented as an add-in to a spreadsheet application  
           [0023]    In general, in another aspect, the invention is a computer apparatus for extracting multi-dimensional data from a spreadsheet. The apparatus includes a central processing unit, random-access memory, a storage device, and devices for user input and output interconnected by a bus, together with computer-readable instructions. The instructions are capable of causing the processing unit to perform the steps of: providing a multi-dimensional data storage that has a cube data definition language, and providing the spreadsheet in a spreadsheet application that has a language for spreadsheet expressions. The spreadsheet expressions describe calculation relationships among data entities in the spreadsheet application. The spreadsheet contains a plurality of spreadsheet expressions. The method further includes parsing the plurality of spreadsheet expressions, and transforming the set of spreadsheet expressions into a set of cube expressions for defining a set of cube entities. The cube entities include dimensions, and the cube expressions conform to the cube data definition language. Each spreadsheet expression corresponds to a cube expression. The calculation relationships among data entities in the spreadsheet application are transformed into corresponding calculation relationships among the cube entities. The method further includes causing the set of spreadsheet expressions to create the corresponding calculation relationships within the multi-dimensional data storage, and moving fact data from the spreadsheet expressions into the corresponding cube entities, where the moving uses the correspondence defined during the transforming step.  
           [0024]    The invention makes it possible to move data and calculations, initially provided in a spreadsheet-compatible format, into a cube. The cube may provide views, operations, optimized response times to certain queries, or other information processing features that were not available to the user before the move.  
           [0025]    The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims. 
       
    
    
     DESCRIPTION OF DRAWINGS  
       [0026]    [0026]FIG. 1A is a block diagram of a spreadsheet application with processes for multi-dimensional data extraction and editing.  
         [0027]    [0027]FIG. 1B is a block diagram of a computing platform for a spreadsheet application.  
         [0028]    [0028]FIG. 1C is a block diagram of a spreadsheet application with a wizard process.  
         [0029]    [0029]FIG. 1D is a block diagram of a spreadsheet application with an add-in facility.  
         [0030]    [0030]FIG. 2 is a flowchart of an extraction process.  
         [0031]    [0031]FIG. 3 is a flowchart of a setup process.  
         [0032]    [0032]FIG. 4 is a flowchart of a rule extractor process.  
         [0033]    [0033]FIG. 5 is a flowchart of a scanner process.  
         [0034]    [0034]FIG. 6 is a flowchart of a consolidator process.  
         [0035]    [0035]FIG. 7 is a flowchart of a left-hand side consolidator function.  
         [0036]    [0036]FIG. 8A shows an example spreadsheet.  
         [0037]    [0037]FIG. 8B shows an example spreadsheet with cell contents replaced by captions.  
         [0038]    [0038]FIG. 9 is a block diagram of example multi-dimensional data structures.  
         [0039]    Like reference symbols in the various drawings indicate like elements. 
     
    
     DETAILED DESCRIPTION  
       [0040]    In one embodiment, with reference to FIG. 1A, a spreadsheet application  22  has an extraction process  30  for extracting multidimensional data into a cube  60 . The spreadsheet application  22  is implemented in software running on a computing platform  63 , shown in FIG. 1B.  
         [0041]    Overview  
         [0042]    As will be described in more detail below, a user, not shown, can apply the extraction process  30  to spreadsheet-based calculations in order to configure the cube  60 . This enables the user to move information structures and (optionally) the information itself from a spreadsheet into a cube.  
         [0043]    An advantage of the described embodiment is that the user can use the spreadsheet application  22  as an information-analyzing environment for information in the cube  60 . This can be especially useful when the user is already familiar with the use of intrinsic information analysis tools  225  in the spreadsheet application  22 . Intrinsic information analysis tools  225  may include features for formatting and exporting information as well as analytical tools such as what-if scenarios, problem solving, numeric calculations, and other features known to those skilled in the art. The range of tools intrinsic to the spreadsheet application  22  is not central to the described invention and will not be described exhaustively here; the tools  225  are cited, among other reasons, to show a benefit to using a spreadsheet application  22  with respect to multi-dimensional data access.  
         [0044]    Another benefit to using a cube  60  is that the cube  60  may include features that were not intrinsically available from within the spreadsheet application  22 , such as the ability to view a slice that intersects the dimensional hierarchies of the cube  60  at various levels. Also, the engine of a multi-dimensional database will often pre-compute the aggregations on its measures, providing significantly improved response times (as compared with queries that are not pre-computed).  
         [0045]    Additional benefits can occur when the data is initially provided in a spreadsheet-compatible format but would be more useful in the cube  60 , or in any case when the user considers the spreadsheet application  22  to be their tool-of-choice for information analysis.  
         [0046]    Computing Environment  
         [0047]    [0047]FIG. 1A shows a spreadsheet application  22  that can access a cube storage  62  via data interface services  64 . In the present embodiment, the spreadsheet application  22  is Excel. The data interface services  64  includes ADO (Active Data Objects) and DAO (Data Access Objects) implementations such as those provided by Microsoft. The data interface services  64  may also include ProClarity connectivity. ProClarity is manufactured by ProClarity Corporation, Inc. Using ADO, DAO, and ProClarity to provide data services to software applications is well known in the art.  
         [0048]    In the present embodiment, the cube storage  62  may include software for database and other data storage services, such as Microsoft® Access 2000 and the Microsoft Jet database engine, or Microsoft® SQL Server™ 2000, all of which are products of Microsoft Corp. The cube storage  62  supports a DML (data manipulation language) appropriate to the data storage software, such as MDX (Multidimensional Expressions) for Microsoft SQL Server 2000 Analysis Services. The cube storage  62  may be a database or a combination of databases. The cube storage  62  includes a cube  60  that can act as a multidimensional data source. The cube  60  contains structures for data and can also contain the data itself. The cube  60  may have as few as one dimension or as many dimensions as its storage devices and underlying software will support. (In an unconfigured state, the cube  60  has no dimensions.) The spreadsheet application  22  has access to a variety of services and devices, shown in FIG. 1B. The spreadsheet application  22  runs on a computing platform 63 that includes an operating system  631  such as Microsoft Windows 98. The operating system  631  is a software process, or set of computer instructions, resident in either main memory  634  or a storage device  637  or both.  
         [0049]    A processor and motherboard  633  contains at least one processor that can access main memory  634  to execute the computer instructions that describe the operating system  631  and the spreadsheet application  22 .  
         [0050]    The user interacts with the computing platform via an input device  632  and an output device  636 . For Windows 98, possible input devices  632  include a keyboard, a microphone, a touch-sensitive screen and a pointing device such as a mouse; possible output devices  636  include a display screen, a speaker, and a printer.  
         [0051]    The storage device  637  includes a computer-writable and computer-readable medium, such as a disk drive. A bus  635  interconnects the processor and motherboard  633 , the input device  632 , the output device  636 , the storage device  637 , main memory  634 , and optional network connection  638 . The network connection  638  includes a device and software driver to provide network functionality, such as an Ethernet card configured to run TCP/IP, for example.  
         [0052]    As is known in the art, when a network connection  638  is present and connected to a network with other hosts, not shown, the cube storage  62  need not be hosted on the same computing platform as the spreadsheet application  22 . That is, cube storage  62  may be available remote via a network connection  638 . For instance, the data interface services  64  may perform data remoting services transparently to the spreadsheet application  22 , as is well known in the art. For the sake of simplicity, however, the description of the present embodiment will refer to cube storage  62  as though it were local to the spreadsheet application  22 .  
         [0053]    In the present embodiment, the extraction process  30  is written in the programming environment Microsoft® Visual Basic™, which is another product of Microsoft Corp. Some components may be written in other languages such as C++ or Delphi and incorporated into the main body of software code via component standards such as COM (Common Object Model) or OLE (Object Linking and Embedding), as is known in the art.  
         [0054]    Extraction  
         [0055]    In one embodiment, an extraction process  30  converts formulas in a Microsoft Excel spreadsheet into MDX calculation statements. The extraction process  30  is a software program or set of computer instructions capable of interacting with the spreadsheet application  22  via interfaces that the spreadsheet exposes, such as OLE or an internal scripting environment like Visual Basic for Applications. The extraction process  30  can configure a cube  60  based on the spreadsheet formulas and can populate the cube  60  with data from the spreadsheet.  
         [0056]    With reference to FIG. 2, the extraction process  30  includes a setup process  32 , a rule extractor  34 , a structure instantiation process  36 , and a migration process  38 . The extraction process  30  can use resources including a spreadsheet collection  26 , a spreadsheet syntax model  33 , and a multi-dimensional syntax model  37 . The extraction process  30  may at times access cube storage  62 , as will be explained in more detail.  
         [0057]    The extraction process  30  invokes the setup process  32  before the rule extractor  34 , the rule extractor  34  before the structure instantiation process  36 , and the structure instantiation process  36  before the migration process  38 .  
         [0058]    Extraction Setup  
         [0059]    In general, the setup process  32  establishes computing environments and data sources for use by subsequent processes.  
         [0060]    With reference to FIG. 3, the setup process  32  includes a spreadsheet selector  121  and a cube environment selector  123 . The spreadsheet selector  121  adds at least one spreadsheet sheet  27  to the spreadsheet collection  26 . The spreadsheet sheet  27  and the spreadsheets in the spreadsheet collection  26  are compatible with Excel. The spreadsheet selector  121  may also specify a range within a spreadsheet sheet  27  to which the extraction process  30  will be confined. The range may have portions demarked as metadata, i.e. data that describes other data within the range. Example metadata includes column and row headers.  
         [0061]    The cube environment selector  123  specifies a cube  60  and configures connections via data interface services  64 . If the cube  60  is not yet configured, the cube environment selector  123  may specify dimensions and members to be added to the cube  60 .  
         [0062]    The setup process  32  may also map regions of a spreadsheet sheet  27  to elements of the cube  60 . For many spreadsheet formats, this may be as simple as mapping metadata values to members and dimensions of the cube  60 .  
         [0063]    Rule Extractor  
         [0064]    The extraction process  30  invokes the rule extractor  34  after the setup process  32 . In general, with reference to FIG. 4, the rule extractor  34  examines the spreadsheet collection  26  for calculations expressed in a syntax (described in a spreadsheet syntax model  33 ), converts the calculations to expressions appropriate to a multi-dimensional syntax model  37 , and normalizes the multi-dimensional calculation expressions by consolidating, validating, and ranking them. One objective of the normalization is to ensure that interdependencies between the multi-dimensional calculation expressions do not cause problems in the multi-dimensional calculation environment. Examples of potential problems include circular references, reference antecedence errors, and unnecessary inefficiencies.  
         [0065]    The extraction process  30  also distinguishes spreadsheet cells that contain factual data from spreadsheet cells that contain calculations. This latter category can be called “derivative” values. A derivative cell contains a calculation expression (or a reference to a calcuation expression) that computes its value based on the values of other cells or functions.  
         [0066]    The rule extractor  34  includes a scanner  342 , a consolidator  344 , a validater  346 , a ranker  348 , and a transformer  349 . In addition, the rule extractor  34  includes a cell range  341  and a multi-dimensional expressions list  343 .  
         [0067]    The rule extractor  34  invokes the scanner  342  before invoking the consolidator  344 , the consolidator  344  before the validater  346 , and the validater  346  before the ranker  348 .  
         [0068]    Scanner  
         [0069]    The scanner  342  examines the spreadsheet collection  26  for information to extract. Information relevant to the scanner  342  includes calculations used within a spreadsheet and entities used as inputs and outputs of such calculations. The scanner 342 identifies instances of this information and makes it available for use by subsequent processes.  
         [0070]    With reference to FIG. 5, the scanner  342  determines a range to scan and a spreadsheet syntax to use (step  401 ). Specifically, the scanner  342  receives a reference to a spreadsheet syntax model  33  (step  401 ). The spreadsheet syntax model  33  will be used to examine the contents of cells in the spreadsheets in the spreadsheet collection  26 . With Excel, the spreadsheet syntax model  33  models the syntax of the formulas that can be entered into cells. One use of the spreadsheet syntax model  33  is to identify operations such as “SUM” and “AVERAGE” that have counterparts within the operations applicable to the cube  60 , so that the spreadsheet expressions can be mapped to the cube expressions.  
         [0071]    The scanner  342  also receives a reference to a cell range  341  (also in step  401 ). The cell range  341  specifies which cells in the spreadsheets in the spreadsheet collection  26  are to be examined by the scanner  342 .  
         [0072]    The scanner  342  begins at a first cell in the cell range  341  (step  402 ). The scanner  342  parses the current cell for candidate content (step  403 ). Candidate content includes calculations expressed in the spreadsheet syntax described in the spreadsheet syntax model  33  determined in step  401 . These calculations may include functions, variables, literals, cell references, operators, and special tokens. When the spreadsheet is Excel, examples of special tokens include brackets, colons, and exclamation marks.  
         [0073]    The scanner  342  evaluates whether the current cell contains candidate content (step  404 ). If candidate content is present (step  404 ), the scanner  342  translates the candidate content into a multi-dimensional calculation expression, fitting the multi-dimensional syntax model  37  (step  405 ). The scanner  342  then adds the multi-dimensional calculation expression to the multi-dimensional expressions list  343  (step  406 ). However, if candidate content was not present in step  404 , the scanner  342  moves directly to evaluation step  407 .  
         [0074]    The scanner  342  evaluates whether there are more cells to scan (step  407 ). If there are, the scanner  342  goes to the next cell in the range of cells (step  408 ) and another parsing begins (step  403 ); otherwise, the scanner  342  halts (step  409 ).  
         [0075]    Consolidator  
         [0076]    In general, the consolidator  344  tries to eliminate unnecessary repetitions inside the multi-dimensional expressions list  343 . The consolidator  344  checks the syntax of these expressions for opportunities to combine and thereby simplify them. Broadly speaking, if the multi-dimensional expressions list  343  contains a set of expressions which specify a rule with particularity over every possible element of a dimension, this is unnecessary detail; it should be possible to express the rule once, with generality, to cover the particular cases at the wider scope of the entire dimension. Likewise, with hierarchical dimensions, there can be similar opportunities for consolidation, if not to the scope of the entire dimension, perhaps within the scope of a higher level in the hierarchy. The consolidator  344  operates on the multi-dimensional expressions list  343  to try to replace exhaustive, narrowly-scoped expressions with fewer, broadly-scoped expressions whose behavior will be functionally equivalent.  
         [0077]    With reference to FIG. 6, the consolidator  344  includes subroutines for a right-hand side (RHS) check  52  and a left-hand side (LHS) check  54 . The consolidator  344  invokes the RHS check  345  before the LHS check  541 .  
         [0078]    The RHS check  345  tries to eliminate right-hand-side repetitions. The RHS check  345  checks each expression in the multi-dimensional expressions list  343 . The RHS check  345  tests the right-hand side of the expression to see if any dimension member is common to every component on that side. If a common member is found, it is eliminated from the RHS of the expression. As an example using MDX, consider the following calculation rule:  
         SALES.TOTAL=SALES.NORTH+SALES.SOUTH  
         [0079]    RHS check  345  finds the dimension member SALES to be common to all components, so RHS check  345  eliminates it from the right hand side, leaving:  
         SALES.TOTAL=NORTH+SOUTH  
         [0080]    The RHS check  345  runs to completion before the LHS check  541  is begun.  
         [0081]    The LHS check  541  normalizes expressions in the multi-dimensional expressions list  343  based on LHS properties of the multi-dimensional expressions. Specifically, LHS check  541  scans the multi-dimensional expressions list  343  for expressions having identical RHS&#39;s. For each such group of expressions having identical RHS&#39;s, the LHS check  541  uses the LHS consolidator function  347  to test whether to consolidate the group into a single expression. The LHS may be applied to the multi-dimensional expressions list  343  repeatedly, until no more consolidations occur.  
         [0082]    First, the LHS check  541  scans the multi-dimensional expressions list  343  for expressions having identical right-hand sides (step  542 ).  
         [0083]    Then, LHS check  541  examines the first such group (step  543 ).  
         [0084]    Next, the LHS check  541  invokes the LHS consolidator function  347  (step  544 ). If the LHS consolidator function  347  so indicates, the LHS check  541  consolidates the group into a single expression (step  545 ); otherwise, the LHS check  541  goes on to test whether there is another group of expressions having identical RHS&#39;s left to test (step  547 ). A positive test leads the LHS check  541  to the next such RHS group (step  548 ) and another examination begins (step  543 ); otherwise, the LHS check  541  halts (step  549 ).  
         [0085]    LHS Consolidator Function  
         [0086]    The LHS consolidator function  347  considers properties of the left hand side of expressions in the multi-dimensional expressions list  343 , returning a positive result if it determines the group can be consolidated into a single expression, and returning a negative result otherwise.  
         [0087]    With reference to FIG. 7, the LHS consolidator function  347  receives from step  544  a group of multi-dimensional expressions having identical RHS&#39;s (step  561 ). The LHS consolidator function  347  then performs up to four tests; if any is positive, the function  56  returns a positive result (step  565 ). Otherwise, the function is negative (step  569 ).  
         [0088]    First, the LHS consolidator function  347  tests whether all members of a dimension are quoted on the left hand side of the equation (a rule per member) (step  562 ).  
         [0089]    Second, the LHS consolidator function  347  tests whether all members of a level of a dimension are quoted on the left hand side of the equation (a rule per member) (step  564 ).  
         [0090]    Third, the LHS consolidator function  347  tests whether all descendants of a member are quoted on the left hand side of the equation (one rule per member) (step  566 ).  
         [0091]    Fourth, the LHS consolidator function  347  tests whether all descendants of a member at a level are quoted on the left hand side of the equation (one rule per member) (step  568 ).  
         [0092]    As an example, suppose again that the multi-dimensional syntax model  37  contains a syntax for MDX. Consider two expressions:  
         SALES.TOTAL=NORTH+SOUTH  
         COST.TOTAL=NORTH+SOUTH  
         [0093]    The right hand side of the expression is the same in both rules. If SALES and COST match any of the criteria specified above then the two rules are reduced into one:  
         TOTAL=NORTH+SOUTH  
         [0094]    If SALES and COST do not match the criteria then the two rules remain as separate rules.  
         [0095]    In another embodiment, steps  562 ,  564 ,  566 , and  568  may be performed in a different sequence (not shown).  
         [0096]    Validater  
         [0097]    The validater  346  ensures that all expressions in the multi-dimensional expressions list  343  contain the same dimensions on both sides of the expression. If the validater  346  detects an error, the relevant expression is ignored and highlighted.  
         [0098]    One feature of the validater  346  is that it detects whether the spreadsheet collection  26  is poorly configured. When the calculation expressions are well structured and complete, the resulting multi-dimensional expressions list  343  will always pass this test. One advantage of performing this test before attempting to configure the cube  60  is that the extraction process  30  can warn the user and allow the user to correct the problem.  
         [0099]    Ranker  
         [0100]    The ranker  348  attempts to ensure the calculations represented by the expressions in the multi-dimensional expressions list  343  will be performed in the right sequence, i.e. a calculated value required by a second expression must be calculated before the second expression. The ranker  348  works out the depth of each expression in the multi-dimensional expressions list  343  by a recursive process, selecting each member of the right hand side of the equation in turn and checking for its existence on the left hand side of each of the other (n −1) expressions. Each time the member is found its depth is increased. The ranker  348  thereby re-organizes the multi-dimensional expressions list  343  with the higher depth values first.  
         [0101]    Note that it is possible for circular references to occur in the multi-dimensional expressions list  343  such that no ordering will satisfy all precedence constraints, as in the following example list:  
         [0102]    [Sales].[Abrams]=[Sales].[Brown]*0.25  
         [0103]    [Sales].[Brown]=[Sales].[Chen]* 0.45  
         [0104]    [Sales].[Chen]=[Sales].[Abrams]* 0.33  
         [0105]    The ranker  348  detects the existence of circular references within the multi-dimensional expressions list  343  and returns a descriptive error.  
         [0106]    Note that the re-ordering of the multi-dimensional expressions list  343  is only necessary if the multidimensional calculation environment (in this embodiment, MDX) processes the multi-dimensional expressions list  343  “procedurally,” i.e., the behavior is dependent on the order in which the expressions are submitted. Though at present MDX does behave procedurally on certain inputs, conceivably this might someday change. For instance, MDX might pre-process expressions differently, or another multidimensional calculation environment might offer the essential functionality of the ranker  348  as an inherent feature of the back-end engine. In that event, the ranker  348  could be an optional process. However, there is still some advantage to detecting error conditions early, so that the user can be informed and allowed to correct the errors without delay.  
         [0107]    Instantiation Process  
         [0108]    The extraction process  30  may invoke a structure instantiation process  36 . The structure instantiation process  36  uses the expressions in the multi-dimensional expressions list  343  to generate statements appropriate to the multi-dimensional syntax model  37 . Each such statement creates a calculated cell rule.  
         [0109]    With reference to FIG. 13, if the dimensions  61   a ,  61   b ,  61   c , etc., have not yet been created, the instantiation process  36  may do so.  
         [0110]    Migration Process  
         [0111]    The extraction process  30  may invoke the migration process  38 . The migration process  38  moves fact data from the spreadsheet collection  26  into the cube  60 . Fact data, in this context, is the value of any spreadsheet cell that maps to an intersect of the cube  60 .  
         [0112]    The migration process  38  must determine, for each piece of fact data to be moved, where to store it within the cube  60 . Then it is a straightforward matter to issue a series of commands to the cube  60  via the data interface services  64  to migrate the data. The fact data must be associated with a measure. The user could specify this information for every piece of data, although that approach can be laborious. An approach less burdensome to the user is to submit the spreadsheet collection  26  in a format that allows the migration process  38  to infer each piece of data&#39;s dimensional membership based on its position within the spreadsheet. For instance, if the data is presented in rows and columns within a demarked area of the spreadsheet, and the leftmost columns and topmost rows of the demarked area contain labels that can be mapped to dimensions of the cube  60 , all the user need specify is the relationship between the labels and the dimensions. These topmost rows are column headings, and the leftmost columns are row headings. It is then straightforward to correlate the pieces of data to the headings on the rows and columns in which they lie. The user need only provide the relation between the headings and the dimensions of the cube  60 , and even this step can be automated if the labels can be mapped, via a formula, to the names of cube dimensions.  
         [0113]    Extraction Process Example  
         [0114]    It may be helpful to step through an example of the extraction process  30  in action. For clarity of explanation, we have chosen a straightforward example; many practical deployments of the extraction process  30  would be more complex.  
         [0115]    [0115]FIG. 8A shows a display spreadsheet  94   a , which is a sample such as might belong to the spreadsheet collection  26  after selection by the spreadsheet selector  121  (see FIG. 2). For this example, this will be the only spreadsheet  27  in the spreadsheet collection  26 .  
         [0116]    Similarly, FIG. 8B shows a captioned spreadsheet  94   b , which is a version of the display spreadsheet  94   a  where the cell display values have been replaced with captions that describe the contents of each cell within the cell range (i.e., not including the column and row headers). For instance, cell D6 shows the display value “250” in the display spreadsheet  94   a . This value is captioned “Fact value” in the captioned spreadsheet  94   b  to indicate that this is a simple data value. In contrast, cell D8 shows the display value “750” in the display spreadsheet  94   a , but its caption in the captioned spreadsheet  94   b  indicates that D8 is not a simple data value but contains a calculation expression. Specifically, cell D8&#39;s calculation expression is a derived value, being the sum of the values in the cell range between cells D6 and D7.  
         [0117]    In the display spreadsheet  94   a , the range of cells selected by the spreadsheet selector  121  goes from B4 to F22, where row  4  and columns B and C are demarked as metadata, and range D5-F22 contains data values. FIG. 9 shows cube elements such as might belong to a cube  60  selected by the cube environment selector  123  for the display spreadsheet  94   a.    
         [0118]    The scanner  342  analyzes every cell for candidate content—in this case, an Excel-based formula. The scanner  342  produces a multi-dimensional expressions list  343 . The sample output of this list is shown in Table 1, with index numbers added to the expressions for clarity. (The index numbers need not appear in the multi-dimensional expressions list  343  itself.)  
                   TABLE 1                           1.   ([Measures].[Total Sales], [Time].[Jan], [Region].[North America]) = SUM(           ([Measures].[Home Sales], [Time].[Jan], [Region].[North America]),           ([Measures].[Export Sales], [Time].[Jan], [Region].[North America]))       2.   ([Measures].[Total Sales], [Time].[Feb], [Region].[North America]) = SUM(           ([Measures].[Home Sales], [Time].[Feb], [Region].[North America]),           ([Measures].[Export Sales], [Time].[Feb], [Region].[North America]))       3.   ([Measures].[Total Sales], [Time].[Mar], [Region].[North America]) = SUM(           ([Measures].[Home Sales], [Time].[Mar], [Region].[North America]),           ([Measures].[Export Sales], [Time].[Mar], [Region].[North America]))       4.   ([Measures]. [Profit], [Time].[Jan], [Region].[North America]) =           ([Measures].[Total Sales], [Time].[Jan], [Region].[North America]) −           ([Measures].[Cost], [Time].[Jan], [Region].[North America])       5.   ([Measures].[Profit], [Time].[Feb], [Region].[North America]) =           ([Measures].[Total Sales], [Time].[Feb], [Region].[North America]) −           ([Measures].[Cost], [Time].[Feb], [Region].[North America])       6.   ([Measures].[Profit], [Time].[Mar], [Region].[North America]) =           ([Measures].[Total Sales], [Time].[Mar], [Region].[North America]) −           ([Measures].[Cost], [Time].[Mar], [Region].[North America])       7.   ([Measures][Total Sales], [Time].[Jan], [Region].[South America]) = SUM(           ([Measures]. [Home Sales], [Time].[Jan], [Region].[South America]),           ([Measures].[Export Sales], [Time].[Jan], [Region].[South America]))       8.   ([Measures].[Total Sales], [Time].[Feb], [Region].[South America]) = SUM(           ([Measures].[Home Sales], [Time].[Feb], [Region].[South America]),           ([Measures].[Export Sales], [Time].[Feb], [Region].[South America]))       9.   ([Measures].[Total Sales], [Time].[Mar], [Region].[South America]) = SUM(           ([Measures].[Home Sales], [Time].[Mar], [Region].[South America]),           ([Measures].[Export Sales], [Time].[Mar], [Region].[South America]))       10.   ([Measures].[Profit], [Time].[Jan], [Region].[South America]) =           ([Measures].[Total Sales], [Time].[Jan], [Region].[South America]) −           ([Measures].[Cost], [Time].[Jan], [Region].[South America])       11.   ([Measures].[Profit], [Time].[Feb], [Region].[South America]) =           ([Measures].[Total Sales], [Time].[Feb], [Region].[South America]) −           ([Measures].[Cost], [Time].[Feb], [Region].[South America])       12.   ([Measures].[Profit], [Time].[Mar], [Region].[South America]) =           ([Measures].[Total Sales], [Time].[Mar], [Region].[South America]) −           ([Measures].[Cost], [Time].[Mar], [Region].[South America])       13.   ([Measures].[Home Sales], [Time].[Jan], [Region].[Americas]) =           ([Measures].[Home Sales], [Time].[Jan], [Region].[North America]) +           ([Measures].[Home Sales], [Time].[Jan], [Region].[South America])       14.   ([Measures].[Home Sales], [Time].[Feb], [Region].[Americas]) =           ([Measures].[Home Sales], [Time].[Feb], [Region].[North America]) +           ([Measures].[Home Sales], [Time].[Feb], [Region].[South America])       15.   ([Measures].[Home Sales], [Time].[Mar], [Region].[Americas]) =           [Measures].[Home Sales], [Time].[Mar], [Region].[North America]) +           ([Measures].[Home Sales], [Time].[Mar], [Region].[South America])       16.   ([Measures].[Export Sales], [Time].[Jan], [Region].[Americas]) =           [Measures].[Export Sales], [Time].[Jan], [Region].[North America]) +           ([Measures].[Export Sales], [Time].[Jan], [Region].[South America])       17.   ([Measures].[Export Sales], [Time].[Feb], [Region].[Americas]) =           [Measures].[Export Sales], [Time].[Feb], [Region].[North America]) +           ([Measures].[Export Sales], [Time].[Feb], [Region].[South America])       18.   ([Measures].[Export Sales], [Time].[Mar], [Region].[Americas]) =           [Measures].[Export Sales], [Time].[Mar], [Region].[North America]) +           ([Measures].[Export Sales], [Time].[Mar], [Region].[South America])       19.   ([Measures].[Total Sales], [Time].[Jan], [Region].[Americas]) =           [Measures].[Total Sales], [Time].[Jan], [Region].[North America]) +           ([Measures].[Total Sales], [Time].[Jan], [Region].[South America])       20.   ([Measures].[Total Sales], [Time].[Feb], [Region].[Americas]) =           [Measures].[Total Sales], [Time].[Feb], [Region].[North America]) +           ([Measures][Total Sales], [Time].[Feb], [Region].[South America])       21.   ([Measures].[Total Sales], [Time].[Mar], [Region].[Americas]) =           [Measures].[Total Sales], [Time].[Mar], [Region].[North America]) +           ([Measures].[Total Sales], [Time].[Mar], [Region].[South America])       22.   ([Measures].[Cost], [Time].[Jan], [Region].[Americas]) =           [Measures].[Cost], [Time].[Jan], [Region].[North America]) +           ([Measures].[Cost], [Time].[Jan], [Region].[South America])       23.   ([Measures].[Cost], [Time].[Feb], [Region].[Americas]) =           [Measures].[Cost], [Time].[Feb], [Region].[North America]) +           ([Measures].[Cost], [Time].[Feb], [Region].[South America])       24.   ([Measures].[Cost], [Time].[Mar], [Region].[Americas]) =           [Measures].[Cost], [Time].[Mar], [Region].[North America]) +           ([Measures].[Cost], [Time].[Mar], [Region].[South America])       25.   ([Measures].[Profit], [Time].[Jan], [Region].[Americas]) =           [Measures].[Profit], [Time].[Jan], [Region].[North America]) +           ([Measures].[Profit], [Time].[Jan], [Region].[South America])       26.   ([Measures].[Profit], [Time].[Feb], [Region].[Americas]) =           [Measures].[Profit], [Time].[Feb], [Region].[North America]) +           ([Measures].[Profit], [Time].[Feb], [Region].[South America])       27.   ([Measures].[Profit], [Time].[Mar], [Region].[Americas]) =           [Measures].[Profit], [Time].[Mar], [Region].[North America]) +           ([Measures].[Profit], [Time].[Mar], [Region].[South America])                  
 
         [0119]    Using the example, if the scanner  342  starts with the first data row and moves across each data column, the first cell found to have candidate content is D8. Cell D8 has the formula: “SUM(D6:D7)”. After parsing the formula, the components are: function SUM, and a range of cells from D6 to D7. The list of components generated by the parse algorithm is then converted into MDX format as follows:  
         SUM=&gt;SUM  
         D6=&gt;([Measures].[Home Sales], [Time].[Jan], [Region].[North America])  
         D7=&gt;([Measures].[Export Sales], [Time].[Jan], [Region].[North America])  
         [0120]    The MDX formula for ([Measures].[Total Sales], [Time].[Jan], [Region].[North America]) is equal to:  
         SUM(([Measures].[Home Sales], [Time].[Jan], [Region].[North America]), ([Measures].[Export Sales], [Time].[Jan], [Region].[North America]))  
         [0121]    For the display spreadsheet  94   a , the scanner  342  converts each cell into a tuple by the following three steps. First, for each dimension in turn, if the dimension is defined in a column, the system looks in the column where the dimension is defined for the row on which the current cell is. If a member is found this member will be used. If a member is not found, the system will start moving up a row at a time in the same column until a member is found.  
         [0122]    Second, if the dimension is defined in a row, the system will look on the row where the dimension is defined for the column on which the current cell is. If a member is found this member will be used; otherwise, the system will start moving to the left a column at a time until a member is found.  
         [0123]    Third, if no member is found, the cell is ignored.  
         [0124]    With regard to cell D6, the first dimension is the Measures dimension  91   a , whose members appear in column C. The scanner  342  will check along row  6  to cell C6 and find the caption “Home Sales”, which is associated with the Home Sales member  91   b . The second dimension is the Time dimension  92   a , whose members appear in row  4 . The scanner  342  will check along column D to cell D4 and find “Jan”, which is associated with the January member  92   b . Note that while the caption “Home Sales” matched the name of the Home Sales member  91   b  exactly, the caption “Jan” was not an exact match with the January member  92   b , but a mapping was still possible. For instance, the setup process  12  may have configured this mapping.  
         [0125]    The third dimension relevant to cell D6 is the Region dimension  93 , whose members appear in column B. The scanner  342  will check along row  6  to cell B6 and find a blank cell (no member). The scanner  342  will then start moving up a row at a time until it finds “North America” in cell B5. The search pattern that the scanner  342  adopts may depend on the formatting of the display spreadsheet  94   a.    
         [0126]    At this point, the scanner  342  has located a member for all dimensions; these members are used as the tuple for cell D6 in the multi-dimensional expressions list  343 .  
         [0127]    The extraction process  30  now proceeds to the consolidator  344 . The multi-dimensional expressions list  343  after the completion of the RHS check  345  is shown in Table 2.  
                   TABLE 2                           1.   ([Measures].[Total Sales], [Time].[Jan], [Region].[North America]) = SUM([Measures].[Home           Sales], [Measures].[Export Sales])       2.   ([Measures].[Total Sales], [Time].[Feb], [Region].[North America]) = SUM([Measures].[Home           Sales], [Measures].[Export Sales])       3.   ([Measures].[Total Sales], [Time].[Mar], [Region].[North America]) = SUM([Measures].[Home           Sales], [Measures].[Export Sales])       4.   ([Measures].[Profit], [Time].[Jan], [Region].[North America]) = [Measures].[Total Sales] −           [Measures].[Cost]       5.   ([Measures].[Profit], [Time].[Feb], [Region].[North America]) = [Measures].[Total Sales] −           [Measures].[Cost]       6.   ([Measures].[Profit], [Time].[Mar], [Region].[North America]) = [Measures].[Total Sales] −           [Measures].[Cost]       7.   ([Measures].[Total Sales], [Time].[Jan], [Region].[South America]) = SUM([Measures].[Home           Sales], [Measures].[Export Sales])       8.   ([Measures].[Total Sales], [Time].[Feb], [Region].[South America]) = SUM([Measures].[Home           Sales], [Measures].[Export Sales])       9.   ([Measures].[Total Sales], [Time].[Mar], [Region].[South America]) = SUM([Measures].[Home           Sales], [Measures].[Export Sales])       10.   ([Measures].[Profit], [Time].[Jan], [Region].[South America]) = [Measures].[Total Sales] −           [Measures].[Cost]       11.   ([Measures].[Profit], [Time].[Feb], [Region].[South America]) = [Measures].[Total Sales] −           [Measures].[Cost]       12.   ([Measures].[Profit], [Time].[Mar], [Region].[South America]) = [Measures].[Total Sales] −           [Measures].[Cost]       13.   ([Measures].[Home Sales], [Time].[Jan], [Region].[Americas]) [Region].[North America] +           [Region].[South America]       14.   ([Measures].[Home Sales], [Time].[Feb], [Region].[Americas]) [Region].[North America] +           [Region].[South America]       15.   ([Measures].[Home Sales], [Time].[Mar], [Region].[Americas]) = [Region].[North America] +           [Region].[South America]       16.   ([Measures].[Export Sales], [Time].[Jan], [Region].[Americas]) = [Region].[North America] +           [Region].[South America]       17.   ([Measures].[Export Sales], [Time].[Feb], [Region].[Americas]) = [Region].[North America] +           [Region].[South America]       18.   ([Measures].[Export Sales], [Time].[Mar], [Region].[Americas]) = [Region].[North America] +           [Region].[South America]       19.   ([Measures].[Total Sales], [Time].[Jan], [Region].[Americas]) = [Region].[North America] +           [Region].[South America]       20.   ([Measures].[Total Sales], [Time].[Feb], [Region].[Americas]) [Region].[North America] +           [Region].[South America]       21.   ([Measures].[Total Sales], [Time].[Mar], [Region].[Americas]) = [Region].[North America] +           [Region].[South America]       22.   ([Measures].[Cost], [Time].[Jan], [Region].[Americas]) = [Region].[North America] +           [Region].[South America]       23.   ([Measures].[Cost], [Time].[Feb], [Region]. [Americas]) = [Region].[North America] +           [Region].[South America]       24.   ([Measures].[Cost], [Time].[Mar], [Region].[Americas]) = [Region].[North America] +           [Region].[South America]       25.   ([Measures].[Profit], [Time].[Jan], [Region].[Americas]) = [Region].[North America] +           [Region].[South America]       26.   ([Measures].[Profit], [Time].[Feb], [Region].[Americas]) = [Region].[North America] +           [Region].[South America]       27.   ([Measures].[Profit], [Time].[Mar], [Region].[Americas]) = [Region].[North America] +           [Region].[South America]                  
 
         [0128]    In this example, the RHS check  345  found unnecessary members among RHS tuples of every expression in the multi-dimensional expressions list  343 . For instance, the first expression was:  
         ([Measures]. [Total Sales], [Time].[Jan], [Region].[North America])=SUM( ([Measures].[Home Sales], [Time]. [Jan], [Region].[North America]), ([Measures].[Export Sales], [Time].[Jan], [Region].[North America]))  
         [0129]    The members [Time]. [Jan] and [Region]. [North America] appear in every tuple in the expression, so they are culled from the RHS to yield the expression shown in Table 2.  
         [0130]    The consolidator  344  now invokes the LHS check  541 . This groups the multi-dimensional expressions list  343  into groups having identical right-hand sides, then applies a multi-part test (the LHS consolidator function  347 ) to determine whether the group can be replaced by more broadly-scoped expression. For instance, with reference to Table 2, expressions 1-3 and 7-9 all have the RHS value:  
         SUM([Measures].[Home Sales], [Measures].[Export Sales])  
         [0131]    Furthermore, expressions 1-3 cite all members of the Time dimension  92   a  on their LHS: namely, the January member  92   b , the February member  92   c , and the March member  92   d . This causes step  562  to flag expressions 1-3 for consolidation into one expression:  
         ([Measures].[Total Sales], [Region].[North America])=SUM([Measures].[Home Sales], [Measures].[Export Sales])  
         [0132]    Likewise, expressions 7-9 would be consolidated, as well:  
         ([Measures].[Total Sales], [Region].[South America])=SUM([Measures].[Home Sales], [Measures].[Export Sales])  
         [0133]    But, still more consolidation is possible. The LHS check  541  can also detect that the two newly-produced expressions still share the same RHS, and furthermore that the expressions contain all the descendants of the Americas member  933 . Therefore step  566  will flag the expressions for consolidation into one final expression:  
         [Measures].[Total Sales]=SUM([Measures].[Home Sales], [Measures].[Export Sales])  
         [0134]    Indeed, the eventual result of the LHS check  541  iterations is that the twenty-seven expressions in the multi-dimensional expressions list  343  reduce to three, as shown in Table 3.  
                   TABLE 3                           1.   [Measures].[Profit] = [Measures].[Total Sales] − [Measures].[Cost]       2.   [Measures].[Total Sales] = SUM([Measures].[Home Sales], [Measures].[Export Sales])       3.   [Region].[Americas] = [Region].[North America] + [Region].[South America]                  
 
         [0135]    The extraction process  30  next moves to the validater  346 , to check that the input expressions contain the same dimensions on both sides of the equation. For this example, the expressions pass the test, so the extraction process  30  moves on the ranker  348 . Note that expression 1 of Table 3 references the Total Sales member  91   d , but that expression is a derived expression defined in expression 2 of Table 3. The ranker  348  compensates by re-ordering the multi-dimensional expressions list  343  to produce the result shown in Table 4.  
                   TABLE 4                           1.   [Measures].[Total Sales] = SUM([Measures].[Home Sales], [Measures].[Export Sales])       2.   [Measures].[Profit] = [Measures].[Total Sales] − [Measures].[Cost]       3.   [Region].[Americas] = [Region].[North America] + [Region].[South America]                  
 
         [0136]    Lastly, each expression in the multi-dimensional expressions list  343  is transformed into a full MDX statement that is applied to the cube  60 .  
         [0137]    Alternate Embodiments  
         [0138]    A number of embodiments of the invention have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention.  
         [0139]    For example, FIG. 1C shows an embodiment in which the extraction process  30  is available via a wizard process  24  within the spreadsheet application  22 . The wizard process  24  includes a dialog that manages a structured sequence of user interactions with predetermined tasks, namely, the steps of the extraction process  30  as disclosed above.  
         [0140]    In a further embodiment, FIG. 1D shows the spreadsheet application  22  having a spreadsheet add-in facility  224  which includes the wizard process  24 . A spreadsheet add-in is a software program configured to install into the spreadsheet application  22  such that the spreadsheet add-in acts as an extension of the features of the spreadsheet application  22 . Such features include the user interface as well as programming interfaces which the spreadsheet add-in facility  224  exposes to the wizard process  24  via a user interface API  226  and a programming API  228 , respectively. The user interface API  226  allows the wizard process  24  to create and control user interface elements, including sheets, menus, and dialogs, within the spreadsheet application  22 . The programming API  228  gives the wizard process  24  access to programming interfaces such as externally manipulable methods and properties of the spreadsheet application  22  itself. The spreadsheet add-in facility  224  for a given spreadsheet application  22  is known in the art; technology and techniques are usually published by the software company that manufactures the spreadsheet application  22 .  
         [0141]    An alternate embodiment of the extraction process  30  could replace the input of the user with a pre-configured set of inputs, for example a batch file containing parameters that the extraction process  30  could read. Yet another embodiment could replace the user with a computer-based algorithm which, while not pre-configured with individual responses, could use heuristics tuned to certain performance goals to provide dynamic input to the extraction process  30 . For instance, an automated computer process might be configured to search a network of file servers, dynamically discovering spreadsheet collections  26  against which to apply the extraction process  30 . Accordingly, in alternative embodiments of the extraction process  30 , the “user” need not be a human but could be any source capable of providing input to the extraction process  30 .  
         [0142]    Alternate embodiments may also include the following. Other spreadsheet applications than Microsoft Excel may be used. Instead of Microsoft Access, the cube storage  62  may be Microsoft SQL Server, or Oracle Enterprise Server, or comparable databases that store multidimensional data. Data definition languages and data manipulation language other than MDX are possible, according to the database used to provide cube storage  62 . The operating system  631  may be Apple MacOS, a handheld device OS, or any OS that can provide a spreadsheet application  22  and appropriate services. Accordingly, other embodiments are within the scope of the following claims.