Datamart DSL

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Datamart DSL

A data mart is a condensed and more focused version of a data warehouse, that reflects the regulations and process specifications of each business unit within an organization. Each data mart is dedicated to a specific business function or region.

Datamart DSL generates for each datamart a java file, in which the SQL query for entities, or MDX query for Cubes, or task client connection for BPM are defined.

The main semantic elements of the Datamart DSL are:

package - The root element that contains all the other elements. A model can contain multiple packages.
import declarations - Used to import external models or even Java classes.
datamart - Defines which data warehouse and which entity/cube/BPM the datamart will use. It contains further elements such as navigation, properties, conditions for entity, derive, axis, slicer for cube and columns, conditions for BPM.
navigation - Used to define joins of entities as “one-to-many” or “many-to-one”.
one to many … to … / many to one … to … - Used to define joins of entities in an MDX query.
properties - Used to define the properties of an entity.
property - Used to define columns and axes for an entity.
conditions - Used to define conditions for entities and BPMs.
condition - Used to define a "Where"-clause for an entity query or conditions for BPM.
derive … from … - Used to define a derived measure of a cube query.
measure - Used in cubes to specify members of a special dimension or a quantity to measure, for example, unit sales of a product, or the cost price of inventory items.
derived - Used for cubes for derived measures defined in the same datamart, instead of the keyword “measure”.
axis - Used for cubes to define the members of the axis, which can be measures, derived measures, or an aggregation or function of a hierarchy.
hierarchy - Used for cubes to define a set of members organized in a structure for convenient analysis. For example, the store hierarchy consists of the store name, city, state, and nation. The hierarchy allows you to form intermediate sub-totals: the sub-total for a state is the sum of the sub-totals of all of the cities in that state, each of which is the sum of the sub-totals of the stores in that city.
slicer - Used for cubes to define the "Where"-clause in a cube query.
columns - Used for BPMs to define one or more columns.
column - Used for BPMs to define which BPM column is used in a datamart.

Datamart definition

► Syntax:

datamart <datamart name> [described by <discription>] 
    from <schema name> persistenceUnit <persistence unit name> 
    using entity/cube/task <entity/cube/task name> [<task mode> <mode name> for task] [nonempty values for cube] {
...
}

Notes:

This generates a <datamart name>Datamart.java file, in which a java class named <datamart name>Datamart is defined. In this class (which is extended from java class ACubeDatamart or AEntityDatamart), the database connection, the querying of entities and cubes and the task-client connection of tasks are defined.
<task name> can be BusinessAdministrator, PotentialOwner, Recipient, TaskInitiator, TaskStakeholder or ExcludedOwner.
<task mode> can be locale (String), host (String) or port (Integer).

► Example:

datamart EmployeeSalary 
    from CCNGDEFAULT persistenceUnit "foodmart" 
    using entity Memployee {. . .}

datamart SalesByState described by "cube based profit review" 
    from CCNGDEFAULT persistenceUnit "foodmart" 
    using cube Sales nonempty values {. . .}

datamart TaskDetails described by "Task details" 
    from BPM persistenceUnit "drools" 
    using task BusinessAdministrator locale "de-DE" {. . .}

entity

► Syntax:

datamart <datamart name> . . . using entity <entity A name> (tracking){
		[navigation {. . .}]
		[properties {. . .}] 
		[conditions {. . .}] 
		[filler rows <INTminRow> to <INTmaxRow>{. . .}] 
	}

navigation

Using the many to one or one to many clause creates a JOIN of entities in a query.

many to one … to entity …

► Syntax:

entity <entity A name> {
    navigation {
        many to one <entity A name>.<entity B reference in entity A> to entity <entity B name> { 
            [navigation {. . . }] 
            [properties {. . . }] 
            [conditions {. . . }] 
        }
    }
    . . .
}

► Example:

datamart EmployeeSalary from CCNGDEFAULT persistenceUnit "foodmart" using entity Memployee {
    navigation { 
        many to one Memployee.position to entity Mposition {
            properties {
                property position_title on axis rows
                property max_scale on axis columns
                property min_scale on axis columns
            }
            conditions {
                condition property pay_type = filtered
            }
        }
    }
    . . .
}

The following Java code is generated:

private String statement = 
    "select Mposition.MAX_SCALE as \"max_scale\",
            Mposition.MIN_SCALE as \"min_scale\", 
            Mposition.POSITION_TITLE as \"position_title\"
            . . .  
    from CCNG.MEMPLOYEE Memployee 
    left join CCNG.MPOSITION Mposition on(Mposition.id=Memployee.position_id) 
    where (Mposition.pay_type = $Mposition.pay_type$) 
    . . .";

private HashMap<Integer, ArrayList<String>> axisMap = new HashMap<Integer,ArrayList<String>>() {{
    put(0,new ArrayList<String>() {{
        . . .
        add("max_scale");
        add("min_scale");
    }});
    put(1,new ArrayList<String>() {{
        . . .
        add("position_title");
    }});
}};

one to many … to entity …

► Syntax:

entity <entity A name> {
    navigation {
        one to many <entity A name>.<entity B reference in entity A> to entity <entity B name> { 
            [navigation {. . . }] 
            [properties {. . . }] 
            [conditions {. . . }] 
        }
    }
    . . .
}

► Example:

datamart SalesByDistrict described by "entity based aggregation of store sales and store cost" 
    from CCNGDEFAULT persistenceUnit "foodmart" 
    using entity Mregion 
{
    navigation {
        one to many Mstore.region to entity Mstore {
            navigation {
                one to many Msales_fact_1998.store to entity Msales_fact_1998 {
                    properties {
                        property store_sales aggregate summation on axis columns
                        property store_cost aggregate summation on axis columns
                    }
                }
            }
        }
    }
    . . .
}

The following Java code is generated:

private String statement = 
    "select SUM(Msales_fact_1998.STORE_SALES) as \"store_sales\",
            SUM(Msales_fact_1998.STORE_COST) as \"store_cost\" 
            . . .
    from CCNG.MREGION Mregion 
    left join CCNG.MSTORE Mstore on(Mstore.region_id=Mregion.id) 
    left join CCNG.MSALES_FACT_1998 Msales_fact_1998 on(Msales_fact_1998.store_id=Mstore.id) 
    where . . . group by . . .";
  
private final HashMap<String, String> filterMap = new HashMap<String,String>() 
{{
    put("$Mregion.id$","&");
    put("$Mstore.id$","&");
    put("$Msales_fact_1998.id$","&");
    . . .
    }};

properties

One or more properties are defined here, each using the property keyword and specifying which kind of aggregation of the property will be queried on which axis.

► Syntax:

properties {
property <entity A property> [aggregate <sql aggregation>] 
[on axis <axis name>] [scale <scale group number>]
. . .
}

Notes:

<sql aggregation> can be average, summation or count.
<axis name> can be columns or rows.
<scale group number> is in the range group1-group9.

on axis rows/columns

► Example:

property sales_region on axis rows
property store_sales _nm on axis columns

The following Java code is generated:

private HashMap<Integer, ArrayList<String>> axisMap = new HashMap<Integer,ArrayList<String>>() {{
            put(0,new ArrayList<String>() {{
                add("store_sales");
                . . .
            }});
            put(1,new ArrayList<String>() {{
                add("sales_region");
                . . .
            }});
    }};

aggregate

<sql aggregation> can be:

average - SQL Avg() function, returns the average of (a subset of) all values in a specified column
summation - SQL Sum() function, returns the sum of (a subset of) all values in a specified column.
count - SQL Count() function, returns the total number of (a subset of) values in a specified column.

► Example:

property store_sales aggregate summation

The following Java code is generated:

private String statement = 
    "select SUM(Msales_fact_1998.STORE_SALES) as \"store_sales\", . . . 
     from . . . left join . . . on(. . .) group by . . . ";

scale

Used to define different groups of scale values (group1-group9) for diagrams and tables.

► Example:

property store_sales scale group1

conditions

► Syntax:

conditions {
     condition 
     [<operator>]
           Number
           /STRING
           /(filtered [optional])|selected
           /property <entity A property> [aggregate <sql aggregation>]
           /entitycolumn <entity C>.<entity C Property>
           /column <task column>
     [<operator>
           Number
           /STRING
           /(filtered [optional])|selected
           /property <entity A property> [aggregate <sql aggregation>]
           /entitycolumn <entity D>.<entity D Property>
           /column <task column>
     ]
}

Notes:

<operator> can be (), =, <, >, <=, >=, like, and or or.
<task column> can be Name, Priority, Status, Subject, Description, ExpirationTime, CreatedOn, CreatedBy, ActivationTime, ActualOwner, TaskId, ProcessId, ProcessInstanceId or ProcessSessionId.

selected

The specified column will be selectable in the GUI.

► Example:

condition property gtin_cd = selected

The following Java code is generated:

private String statement = 
    ". . .
    where (gtin.gtin_cd in (§gtin.gtin_cd§))
    . . .";
private final HashMap<String, String> filterMap = new HashMap<String,String>() 
{{
    . . . 
    put("§gtin.gtin_cd§","select distinct GTIN_CD as \"gtin_cd\" from pod.GTIN");
    }};

filtered

The specified column will be filterable in the GUI. optional means this specified column can be chosen for being filtered or not.

► Example 1:

condition property pay_type = filtered

The following Java code is generated:

private String statement = 
". . .
where (Mposition.pay_type = $Mposition.pay_type$)
. . .";
private final HashMap<String, String> filterMap = new HashMap<String,String>()
{{
    . . .
    put("$Mposition.pay_type$","select distinct PAY_TYPE as \"pay_type\" from CCNG.MPOSITION");
    }};

► Example 2:

condition property education_level = filtered optional

The following Java code is generated:

private String statement = 
    ". . .
    where (($Memployee.education_level$='*' or Memployee.education_level = $Memployee.education_level$))
    . . .";
private final HashMap<String, String> filterMap = new HashMap<String,String>() {{
	. . . 
      put("$Memployee.education_level$","select '*' from dual union select distinct EDUCATION_LEVEL as \"education_level\" from CCNG.MEMPLOYEE");
    }};

filler rows mm to nn

► Syntax:

filler rows <INTminRow> to <INTmaxRow>{
      [fill <entity property> with 
            filler-address city
            | filler-address postalcode
            | filler-date future <INT> years
            | filler-date past <INT> years
            | filler-date range <+-INTby> up to and including <+-INTey> years
            | filler-person full name
            | filler-person first name
            | filler-person last name
            | filler-person email
            | filler-person phone
            | filler-person gender male as <STRINGmt> female as <STRINGft>
            | filler-signed-double range 
              [ <+- Double> | <entity property>] 
              up to and including
              [ <+- Double > | <entity property>]
              with <INT> decimals
              round to < Double >
            | filler-signed-double random (<+-Double >)
            | filler-signed-integer range 
              [ <+-INT> | <entity property>] 
              up to and including
              [ <+-INT> | <entity property>]
              round to <INT>
            | filler-signed-integer random ( <+-INT> )
            | filler-text random ( <STRING> )
            | filler-text paragraps [<INT>]
            | filler-text sentences [<INT>]
            | filler-text words [<INT>]
            | filler-unsigned-double range 
              [ < Double > | <entity property>]
              up to and including
              [ < Double > | <entity property>]
              with <INT> decimals
              round to < Double >
            | filler-unsigned-double random ( < Double > )
            | filler-unsigned-integer range 
              [ <INT> | <entity property>]
              up to and including
              [ <INT> | <entity property>]
              round to <INT>
            | filler-unsigned-integer random ( <INT> )
      ]
}

cube

Use the keyword cube to define the data source cube of a FROM clause in an MDX query.

► Syntax:

datamart <datamart name> . . . using cube <cube name>  [nonempty values] { 
    [derive <derived measure name> from 
        [<derive operator>]
            Number
            /Cube Measure
            /Cube Derived Measure
            /Cube Member Tuple
            /Cube Aggregation
        [<derive operator>]
            [Number
            /Cube Measure
            /Cube Derived Measure
            /Cube Member Tuple
            /Cube Aggregation]
	]
	[axis <axis name> {
            Cube Hierarchy
            /Cube Measure
            /Cube Derived Measure
            /Cube Set Aggregation
        }]
        [slicer 
            Cube Hierarchy
            /Cube Measure
            /Cube Derived Measure
            /Cube Set Aggregation
        ]
}

Keywords

nonempty values

See the sections #NonEmpty() function and #NonEmptyCrossjoin() function later in this document.

► Syntax:

datamart <datamart name> [described by <description>] 
    from <schema name> persistenceUnit <persistence unit name> 
    using cube <cube name> 
    nonempty values 
{
...
}

The following Java code is generated:

private String statement = 
    "select 
    Non Empty{ . . . } on columns, 
    NonEmptyCrossjoin(. . .) on rows 
    from 
    . . .";

derive … from

Creates a Calculate Member.

► Syntax:

derive <derived measure name> from
    [<derive operator>]
        Number
        /Cube Measure
        /Cube Derived Measure
        /Cube Member Tuple
        /Cube Aggregation
    [<derive operator>]
        [Number
        /Cube Measure
        /Cube Derived Measure
        /Cube Member Tuple
        /Cube Aggregation]

Notes:

<derive operator> can be +, -, *, / or ().

► Example 1:

derive SumSales from measure SaleNumber * measure unitPrice

The following Java code is generated:

private String statement = 
    "with member [Measures].[SumSales] as '[Measures].[SaleNumber]*[Measures].[unitPrice]'
     . . .";

► Example 2:

derive MediaTotal from hierarchy PromotionMedia condensed over measure UnitSales

The following Java code is generated:

private String statement = 
    "with member [Measures].[MediaTotal] as '([Measures].[UnitSales],[PromotionMedia].[All Media])'
     . . .";

axis

Use the keyword axis to define the query axes of a SELECT clause in an MDX query.

► Syntax:

axis <axis name> {
    Cube Hierarchy
    /Cube Measure
    /Cube Derived Measure
    /Cube Set Aggregation
}

Notes:

<axis name> can be columns, rows, pages, chapters or sections.

► Example:

axis columns {
    hierarchy PromotionMedia level MediaType detailed ordered by derived MediaPerc descending  
    derived MediaPerc
}

The following Java code is generated:

private String statement = 
    "select CrossJoin(Order([PromotionMedia].[MediaType].Members,[Measures].[MediaPerc],Desc),[Measures].[MediaPerc]) on columns
     . . .";

slicer

Use the keyword slicer to define the slicer axis of a WHERE clause in an MDX query.

► Syntax:

slicer
    Cube Hierarchy
    /Cube Measure
    /Cube Derived Measure
    /Cube Set Aggregation

► Example:

The following Java code is generated:

private String statement = 
    ". . .
     where
     . . .";

Definitions

Cube Measure

measure <cube measure> [scale <scale group number>]

Notes:

<scale group number> can have the values group1-group9, permitting different scales values for diagrams and tables.

Cube Derived Measure

derived <derived measure name defined in this datamart> 
[scale <scale group number>]

Notes:

<scale group number> can have the values group1-group9, permitting different scales values for diagrams and tables.

Cube Hierarchy

hierarchy <hierarchy name> 
    [default] 
    [condensed] 
    [exploded] 
    [level <level name> 
        [filtered|selected]
    ] 
    [detailed 
        [except <level name>] 
        [ordered by 
            [Cube Measure
            /Cube Derived Measure
            ]
            [descending]
        ]
    ]

default

Returns: .DefaultMember of hierarchy, if defaultMember of hierarchy is defined in cubeDSL.

► Example:

dimension PromotionMedia {
      hierarchy hasAll allMemberName="All Media" defaultMember="All Media"{
            . . .
      }
}

Then, we use the keyword default for a hierarchy in datamartDSL, e.g.:

derive MediaTotal from hierarchy PromotionMedia default over measure UnitSales

The following Java code is generated:

private String statement = 
    "with member [Measures].[MediaTotal] as '([Measures].[UnitSales],[PromotionMedia].Defaultmember) 
    . . .";

Returns: .Members of hierarchy, if defaultMember of hierarchy is not defined in cubeDSL.

► Example:

slicer hierarchy Warehouse default

Then, we use the keyword default for a hierarchy in datamartDSL, e.g.:

derive MediaTotal from hierarchy PromotionMedia default over measure UnitSales

The following Java code is generated:

private String statement = 
    "select . . . 
    from . . . 
    where ([Warehouse].Members)";

condensed

Returns: allMemberName of hierarchy, if allMemberName of hierarchy defined in cubeDSL.

► Example:

dimension PromotionMedia {
      hierarchy hasAll allMemberName="All Media" {
            . . .
      }
}

Then, we use the keyword condensed for a hierarchy in datamartDSL, e.g.:

derive MediaTotal from hierarchy PromotionMedia condensed over measure UnitSales

The following Java code is generated:

private String statement = 
    "with member [Measures].[MediaTotal] as '([Measures].[UnitSales],[PromotionMedia].[All Media])'
    . . .";

Returns: .DefaultMember of hierarchy, if defaultMember of hierarchy defined in cubeDSL.

► Example:

dimension PromotionMedia {
      hierarchy hasAll defaultMember="All Media"{
            . . .
      }
}

Then, we use the keyword condensed for a hierarchy in datamartDSL, e.g.:

derive MediaTotal from hierarchy PromotionMedia condensed over measure UnitSales

The following Java code is generated:

private String statement = 
    "with member [Measures].[MediaTotal] as '([Measures].[UnitSales],[PromotionMedia].Defaultmember) 
     . . .";

Returns: .Members of hierarchy if nothing is additionally defined for hierarchy in cubeDSL.

► Example:

slicer hierarchy Warehouse condensed

The following Java code is generated:

private String statement = 
    "select . . . 
    from . . . 
    where ([Warehouse].Members)";

exploded

Returns: .AllMembers of hierarchy.

► Example:

derive MediaTotal from hierarchy PromotionMedia exploded over measure UnitSales

Then, we use the keyword condensed for a hierarchy in datamartDSL, e.g.:

derive MediaTotal from hierarchy PromotionMedia condensed over measure UnitSales

The following Java code is generated:

private String statement = 
    "with member [Measures].[MediaTotal] as '([Measures].[UnitSales],[PromotionMedia].AllMembers)'
     . . .";

level ... filtered

The specified level can be filtered in the GUI.

► Example: in axis

Datamart . . . {
      . . .
        axis rows {
            hierarchy TheTime level Month filtered 
            hierarchy Store level StoreState selected
        }
      . . .
}

The following Java code is generated:

private String statement = 
". . .
Select . . .
Crossjoin($[TheTime].[Month]$,§[Store].[StoreState]§) on rows
. . .";
private final HashMap<String, String> filterMap = new HashMap<String,String>() {{
    put("$[TheTime].[Month]$","select {} on columns,[TheTime].[Month].members on rows from Sales");
    . . .    }};

► Example: in slicer

Datamart . . . {
      . . .
      slicer hierarchy TheTime level Quarter filtered
      slicer hierarchy StoreType level StoreType selected
}

The following Java code is generated:

private String statement = 
    "select . . . 
     from . . . 
     where (?[TheTime].[Quarter]?,![StoreType].[StoreType]!)";
private final HashMap<String, String> filterMap = new HashMap<String,String>() {{
    . . .
    put("?[TheTime].[Quarter]?","select {} on columns,[TheTime].[Quarter].members on rows from Warehouse");
    . . .
    }};

level ... selected

The specified level can be selected in the GUI.

► Example: in axis

Datamart . . . {
      . . .
      axis rows {
          hierarchy TheTime level Month filtered 
          hierarchy Store level StoreState selected
      }
    . . .
}

The following Java code is generated:

private String statement = 
    ". . .
     Select . . .
     Crossjoin($[TheTime].[Month]$,§[Store].[StoreState]§) on rows
     . . .";
private final HashMap<String, String> filterMap = new HashMap<String,String>()
{{
    . . .
    put("§[Store].[StoreState]§","select {} on columns,[Store].[StoreState].members on rows from Sales");    }};

► Example: in slicer

Datamart . . . {
      . . .
      slicer hierarchy TheTime level Quarter filtered
      slicer hierarchy StoreType level StoreType selected
}

The following Java code is generated:

private String statement = 
    "select . . . 
    from . . . 
    where (?[TheTime].[Quarter]?,![StoreType].[StoreType]!)";
private final HashMap<String, String> filterMap = new HashMap<String,String>() {{
    . . .
    put("![StoreType].[StoreType]!","select {} on columns,[StoreType].[StoreType].members on rows from Warehouse");
    }};

detailed

► Returns: .Members of level.

► Example:

axis pages {
    hierarchy StoreType level StoreType detailed
}

The following Java code is generated:

private String statement = 
    "Select [StoreType].[StoreType].Members on pages
     . . .";

► Returns: .Children of level, if the keyword filtered or selected appears before the keyword detailed.

► Example:

axis rows {
      hierarchy TheTime level Month filtered detailed
      hierarchy Store level StoreState selected detailed
}

The following Java code is generated:

private String statement = 
    ". . .
     Select . . .
     Crossjoin($[TheTime].[Month]$.Children,§[Store].[StoreState]§.Children) 
     on rows
     . . .";

except

See the section #Except() function later in this document.

► Example:

hierarchy TheTime level Month detailed except Quarter

The following Java code is generated:

private String statement = 
    ". . .Except([TheTime].[Month].Members,{%[TheTime].[Quarter]%}) 
     . . .";

order by ... (descending)

See the section #Order() function later in this document.

► Example:

hierarchy PromotionMedia level MediaType detailed ordered by derived MediaPerc descending

The following Java code is generated:

private String statement = 
    ". . .
     Order([PromotionMedia].[MediaType].Members,[Measures].[MediaPerc],Desc) 
     . . .";

Cube Member Tuple

► Syntax:

[<function name> / <parameter function name> (INT)
  of ]
      Cube Hierarchy
  over
      Cube Measure

<function name> can be:

previous - see the section #.PrevMember function later in this document.
next - see the section #.NextMember function later in this document.
first - see the section #.FirstChild function later in this document.
last - see the section #.LastChild function later in this document.

<parameter function name> can be:

lag - see the section #.Lag() function later in this document.
lead - see the section #.Lead() function later in this document.

► Example:

derive PrevPeriod from lag(01) of hierarchy TheTime over measure StoreSales

The following Java code is generated:

private String statement = 
    "with member [Measures].[PrevPeriod] as '([Measures].[StoreSales],[TheTime].LAG(1))'
     . . .";

Cube Set Tuple

► Syntax:

<set function name> / <set parameter function name> (INT) 
  of
     Cube Hierarchy

<set function name> can be:

year-to-date - See the section #YTD() function later in this document.
periods - See the section #PeriodsToDate() function later in this document.

<set parameter function name> can be:

tail - See the section #Tail() function later in this document.
head - See the section #Head() function later in this document.

► Example:

derive SumSCount from summation of periods of hierarchy TheTime level Year 
    filtered  over measure SalesCount

The following Java code is generated:

private String statement = 
    "with member [Measures].[SumSCount] as 
     'SUM(PERIODSTODATE($[TheTime].[Year]$),[Measures].[SalesCount])' 
     . . .";

Cube Aggregation

► Syntax:

<aggregation name>
  of
     Cube Set Tuple
     /Cube Hierarchy
  over
     Cube Measure

<aggregation name> can be:

average - See the section #Avg() function later in this document.
summation - See the section #Sum() function later in this document.
aggregate - See the section #Aggregate() function later in this document.
deviation - See the section #StdDev() function later in this document.

► Example:

derive SumSCount from summation of periods of hierarchy TheTime level Year 
    filtered  over measure SalesCount

The following Java code is generated:

private String statement = "with member [Measures].[SumSCount] as 
    'SUM(PERIODSTODATE($[TheTime].[Year]$),[Measures].[SalesCount])' . . .";

CONTINUE HERE

The Basics

MDX (MultiDimensional eXpressions)

MDX is a query language for OLAP databases, much like SQL is a query language for relational databases. It is also a calculation language, with syntax similar to spreadsheet formulas.

MDX has several elements that are used by, or influence, most statements:

Identifiers - Identifiers are the names of objects such as cubes, dimensions, members or measures, e.g. [Time], [Measures].
Data types - Define the types of data that are contained by cells, member properties and cell properties. (For details see the section #Data types later in this document.)
Expressions (MDX) - An expression is a combination of identifiers, values and operators. You can use an expression as part of the data to be retrieved by a query, or as a search condition to look for data that meets a set of criteria. Expressions include functions that return as a single value a set expression, e.g., [Measures].[Discount Amount] * 1.5.
Operators - Operators are syntax elements that work with one or more simple MDX expressions to make more complex MDX expressions, e.g., +, -, *, /, <, >, AND and OR.
Functions - Functions are syntax elements that take zero, one or more input values and return a scalar value or an object, e.g., .Dimension, .Level , IsEmpty(),Order(), .CurrentMember etc.
Comments - Comments are pieces of text that are inserted into MDX statements or scripts to explain the purpose of the statement. MDX supports //, -- and /* ... */ as commenting characters.
Reserved keywords - Reserved keywords are words that are reserved for the use of MDX and should not be used for object names used in MDX statements, e.g., SELECT, WHERE etc.

Data types

There are six primary data types in MDX:

Type	Description
scalar	Scalar is either a number or a string. It can be specified as a literal, e.g., `number 5` or `string "OLAP"`, or it can be returned by an MDX function, e.g., `Aggregate()`, `.UniqueName`, `.Value` etc.
Dimension / Hierarchy	Dimension is a dimension of a cube. A dimension is a primary organizer of measure and attribute information in a cube. MDX does not know of, nor does it assume, any dependencies between dimensions - they are assumed to be mutually independent. A dimension will contain some members organized in some hierarchy or hierarchies, each in turn containing levels. It can be specified by its unique name, e.g., `[Time]` or it can be returned by an MDX function, e.g., `.Dimension`. Hierarchy is a dimension hierarchy of a cube. It can be specified by its unique name, e.g., `[Time].[Fiscal]` or it can be returned by an MDX function, e.g., `.Hierarchy`. Hierarchies are contained within Dimensions.
Level	Level is a level in a dimension hierarchy. It can be specified by its unique name, e.g., `[Time].[Fiscal].[Month]` or it can be returned by an MDX function, e.g., `.Level`.
Member	Member is a member in a dimension hierarchy. It can be specified by its unique name, e.g., `[Time].[Fiscal].[Month].[August 2014]` or its qualified name, e.g., `[Time].[Calendar].[2014].[Q3].[August 2014]`, or it can be returned by an MDX function, e.g., `.PrevMember`, `.Parent`, `.FirstChild` etc. Note that all members are specific to a hierarchy. If the same product is a member of two different hierarchies (`[Product].[ByManufacturer]` and `[Product].[ByCategory]`), then there will be two different members visible that may need to be coordinated in sets and tuples.
Tuple	Tuple is an ordered collection of one or more members of different dimensions. Tuples can be specified by enumerating the members, e.g., (`[Time].[Fiscal].[Month].[August]`, `[Measures].[Sales]`) or returned by an MDX function, e.g., `.Item()`.
Set	Set is an ordered collection of tuples with the same dimensionality, or hierarchality in the case of Microsoft's implementation. It can be specified by enumerating the tuples, e.g., `{([Measures].[Sales], [Time].[Fiscal].[2014]), ([Measures].[Sales], [Time].[Fiscal].[2014])}` or returned by an MDX function or operator, e.g., `Crossjoin()`, `Filter()`, `Order()`, `Descendants()` etc.
other data types	Member properties are equivalent to attributes in the data warehouse sense. They can be retrieved by name in a query through an axis PROPERTIES clause of a query. The scalar data value of a member property for some member can be accessed in an expression through MDX, either by naming the property (e.g., `[Product].CurrentMember.[Sales Price]`) or by using a special access function (e.g., `[Product].CurrentMember.Properties("Sales Price")`). In limited contexts, MDX allows other data types as well - for example Array can be used inside the `SetToArray()` function to specify an array that is not processed by MDX but passed to a user-defined function in an ActiveX library. Objects of other data types are represented as scalar strings indicating the object names, such as measure group name in Microsoft's `MeasureGroupMeasures()` function or KPI(Key Performance Indicator) name in, for example, Microsoft's `KPIValue()` or `KPIGoal()` functions.

Basic Syntax of an MDX query

► Syntax:

-- One of the three ways to write comments
SELECT {set 0} on COLUMNS, /* block comment */
  {set 1} on ROWS // line comment
  ...
  {set n} on AXIS(n)
FROM [cube]
WHERE (tuple) // called "slicer dimension"

Note: Key differences between MDX and SQL

“Cube in, Cube out” for MDX.
Set notation needs to be used after SELECT.
The FROM clause can name only one cube.
The WHERE clause describes the slicer axis (i.e., all the axes that are not query axes) and is filtered by its default members.

Examples

A typical query

► Example:

SELECT {[Time].[Calendar].[Year].[2013], 
   [Time].[Calendar].[Year].[2014]} ON COLUMNS,
  {[Store].[Store Name].MEMBERS} ON ROWS
FROM [Sales]
WHERE ([Measures].[Store Sales])

.MEMBERS and .CHILDREN functions

► Syntax:

Hierarchy.Members
Level.Members
...
Member.Children

Notes:

.Members returns the set of members in a dimension, level, or hierarchy.
.Children returns the set of children of a specified member.

► Example:

SELECT {[Time].[Calendar].[Year].[2014].CHILDREN} ON COLUMNS,
  {[Store].[Store City].MEMBERS} ON ROWS
FROM [Sales]
WHERE ([Measures].[Store Sales])

Calculate Member

► Syntax:

WITH MEMBER parent1.name1 AS ’expression1’
  MEMBER parent2.name2 AS ’expression2’

Notes:

If a calculated member is only required for a single MDX query, you can define that calculated member by using the WITH keyword. A calculated member that is created by using the WITH keyword no longer exists after the query has finished running.

► Example 1:

WITH MEMBER [Time].[Calendar].[Year].[2014].[H1] AS
’[Time].[Calendar].[Year].[2014].[Q1] + [Time].[Calendar].[Year].[2014].[Q2]’
MEMBER [Time].[Calendar].[Year].[2014].[H2] AS
’[Time].[Calendar].[Year].[2014].[Q3] + [Time].[Calendar].[Year].[2014].[Q4]’
SELECT {[Time].[Calendar].[Year].[2014].[H1], 
   [Time].[Calendar].[Year].[2014].[H2]} ON COLUMNS
  [Store].[Store Name].MEMBERS ON ROWS
FROM [Sales]
WHERE ([Measures].[Profit])

► Example 2: define and use new measures

WITH MEMBER [Measures].[ProfitPercent] AS
’([Measures].[Store Sales] – [Measures].[Store Cost]) / ([Measures].[Store Cost])’
SELECT [Time].[Calendar].[Year].[2014].CHILDREN ON COLUMNS,
 [Store].[Store Name].MEMBERS ON ROWS
FROM [Sales]
WHERE ([Measures].[ProfitPercent])

Slicer Dimension

► Example: slice on the [Product] dimension

SELECT {[Time].[Calendar].[Year].[2014].CHILDREN} ON COLUMNS,
  {[Store].[Store City].MEMBERS} ON ROWS
FROM [Sales]
WHERE ([Product].[Product Family].[Drink],
[Measures].[Store Sales])

Filter() function

► Syntax:

FILTER(set, logical expression )

Notes:

Returns the set that results from filtering a specified set based on a search condition.

► Example: if we are only interested in stores whose 2014 unit sales exceed 1000

SELECT {[Time].[Calendar].[Year].[2014].CHILDREN} ON COLUMNS,
   FILTER( {[Store].[Store City].MEMBERS},
           ([Measures].[Unit Sales], [Time].[Calendar].[Year].[2014]) > 1000 ) ON ROWS
FROM [Sales]
WHERE ([Measures].[Store Sales])

Order() function

► Syntax:

ORDER(set, numeric/string expression, [, ASC | DESC | BASC | BDESC]

Notes:

Arranges members of a specified set, optionally preserving or breaking the hierarchy.

► Example 1: list all measures for each city in decreasing order of their sales count

SELECT [Measures].MEMBERS ON COLUMNS,
    ORDER(
        [Store].[Store City].MEMBERS,
        [Measures].[Sales Count], 
        BDESC
    ) ON ROWS
FROM [Sales]

► Example 2: if we are only interested in stores whose name is between “Berlin” and “Heidelberg”

SELECT [Measures].MEMBERS ON COLUMNS,
    ORDER(
        {[Store].[Store City].[Berlin]:[Heidelberg]},
        [Store].CURRENTMEMBER.Name,
        BASC
    ) ON ROWS
FROM [Sales]

Lag() function

► Syntax:

Member.LAG(index)

Notes:

Returns the member that is a specified number of positions before a specified member on the member's level.
.Lag(1) is equivalent to the .PrevMember function.
.Lag(-1) is equivalent to the .NextMember function.

► Example: list all measures for December, 2014

SELECT [Measures].MEMBERS ON COLUMNS,
       [Time].[Fiscal].[Month].[February 2015].LAG(2) ON ROWS
FROM [Sales]

Lead() function

► Syntax:

Member.LEAD(index)

Notes:

Returns the member that is a specified number of positions following a specified member at the member's level.
.Lead(n) is equivalent to the .Lag(-n) function.

► Example: list all measures for December, 2014

SELECT  [Measures].MEMBERS ON COLUMNS,
        [Time].[Fiscal].[Month].[February 2015].LEAD(-2) ON ROWS
FROM [Sales]

PrevMember function

► Syntax:

Member.PREVMEMBER

Notes:

Returns the previous member in the level that contains a specified member.

► Example: list all measures for January, 2014

SELECT  [Measures].MEMBERS ON COLUMNS,
        [Time].[Fiscal].[Month].[February 2015].PREVMEMBER ON ROWS
FROM [Sales]

NextMember function

► Syntax:

Member.NEXTMEMBER

Notes:

Returns the next member in the level that contains a specified member.

► Example: list all measures for March, 2015

SELECT  [Measures].MEMBERS ON COLUMNS,
        [Time].[Fiscal].[Month].[February 2015].NEXTMEMBER ON ROWS
FROM [Sales]

FirstChild function

► Syntax:

Member.FIRSTCHILD

Notes:

Returns the first child of a specified member.
Leaf members have no children and therefore no first child.

► Example: list all measures for the first quarter of 2015, which is the first child of year 2015

SELECT  [Measures].MEMBERS ON COLUMNS,
        [Time].[Calendar].[Year].[2015].FIRSTCHILD ON ROWS
FROM [Sales]

LastChild function

► Syntax:

Member.LASTCHILD

Notes:

Returns the last child of a specified member.
Leaf members have no children and therefore no last child.

► Example: list all measures for March, 2015, which is the last child of the first quarter of the year 2015

SELECT  [Measures].MEMBERS ON COLUMNS,
        [Time].[Calendar].[Quarter].[Q1 2015].LASTCHILD ON ROWS
FROM [Sales]

Head() function

► Syntax:

HEAD(set [ ,count ] )

Notes:

Returns the first specified number of elements of a set.

► Example: show the profit of top-5 cities in terms of sales count

SELECT [Measures].[Profit] ON COLUMNS,
    HEAD( ORDER( {[Store].[Store City].MEMBERS},
                [Measures].[Sales Count],
                BDESC 
               ), 
          5
        ) ON ROWS
FROM [Sales]

Tail() function

► Syntax:

TAIL(set [ ,count ] )

Notes:

Returns the last specified number of elements of a set.

► Example: show the profit of bottom-5 cities in terms of sales count

SELECT [Measures].[Profit] ON COLUMNS,
    TAIL( ORDER( {[Store].[Store City].MEMBERS},
                [Measures].[Sales Count],
                BDESC 
               ), 
          5
        ) ON ROWS
FROM [Sales]

TopCount() function

► Syntax:

TOPCOUNT(set, count [ ,numeric expression ] )

Notes:

Sorts a set in descending order and returns the specified number of elements with the highest values.
The numeric expression is used to evaluate the tuple values. If a numeric expression is not specified, the function returns the set of members in their natural order, without any sorting, behaving like the Head() function.

► Example: show the profit of top-5 cities in terms of sales count

SELECT [Measures].[Profit] ON COLUMNS,
    TOPCOUNT( [Store].[Store City].MEMBERS,
                5,
                [Measures].[Sales Count]
            ) ON ROWS
FROM [Sales]

BottomCount() function

► Syntax:

BOTTOMCOUNT(set, count [ ,numeric expression ] )

Notes:

Sorts a set in ascending order and returns the specified number of elements with the lowest values.
The numeric expression is used to evaluate the tuple values. If a numeric expression is not specified, the function returns the set of members in their natural order, without any sorting, behaving like the Tail() function.

► Example: show the profit of bottom-5 cities in terms of sales count

SELECT [Measures].[Profit] ON COLUMNS,
    BOTTOMCOUNT( [Store].[Store City].MEMBERS,
                5,
                [Measures].[Sales Count]
            ) ON ROWS
FROM [Sales]

TopPercent() function

► Syntax:

TOPPERCENT(set, percentage, numeric expression)

Notes:

Sorts a set in descending order and then returns the set of tuples with the highest values whose cumulative total is equal to or greater than a specified percentage of the total of all values.
Percentage must be a positive value between 0 and 100.

► Example: show the best profit and cities that make up the top 10% of sales count

SELECT [Measures].[Profit] ON COLUMNS,
    TOPPERCENT( [Store].[Store City].MEMBERS,
                10,
                [Measures].[Sales Count]
            ) ON ROWS
FROM [Sales]

BottomPercent() function

► Syntax:

BOTTOMPERCENT(set, percentage, numeric expression)

Notes:

Sorts a set in ascending order and then returns the set of tuples with the lowest values whose cumulative total is equal to or greater than a specified percentage of the total of all values.
Percentage must be a positive value between 0 and 100.

► Example: show the worst profit and cities that make the bottom 10% of sales count

SELECT [Measures].[Profit] ON COLUMNS,
    BOTTOMPERCENT( [Store].[Store City].MEMBERS,
                    10,
                    [Measures].[Sales Count]
                 ) ON ROWS
FROM [Sales]

TopSum() function

► Syntax:

TOPSUM(set, value, numeric expression)

Notes:

Sorts a set and returns the topmost set of elements whose cumulative total is at least a specified value.

► Example: show the smallest set whose cumulative total using the profit measure is at least the sum of 50000, beginning with the members of this set with the highest sales count values

SELECT [Measures].[Profit] ON COLUMNS,
    TOPSUM ( [Store].[Store City].MEMBERS,
             50000,
             [Measures].[Sales Count]
           ) ON ROWS
FROM [Sales]

BottomSum() function

► Syntax:

BOTTOMSUM(set, value, numeric expression)

Notes:

Sorts a set and returns the bottommost set of elements whose sum is equal to or less than a specified value.

► Example: show the smallest set whose cumulative total using the profit measure is at least the sum of 50000, beginning with the members of this set with the smallest number of sales count

SELECT [Measures].[Profit] ON COLUMNS,
    BOTTOMSUM( [Store].[Store City].MEMBERS,
               50000,
               [Measures].[Sales Count]
             ) ON ROWS
FROM [Sales]

Aggregate() function

► Syntax:

AGGREGATE(set [, numeric expression])

Notes:

Returns a number that is calculated by aggregating over the cells returned by the set expression.
If a numeric expression is not provided, this function aggregates each measure within the current query context by using the default aggregation operator that is specified for each measure.
If a numeric expression is provided, this function first evaluates, and then sums, the numeric expression for each cell in the specified set.

► Example: aggregate profit for all the calendar months

WITH MEMBER [Time].[Calendar].[All sales] AS
    AGGREGATE([Time].[Calendar].[Month].MEMBERS, [Measures].[Amount])
SELECT [Time].[Calendar].[All sales] ON COLUMNS,
  [Store].[Store City].MEMBERS ON ROWS
FROM [Sales]
WHERE [Measures].[Profit]

Avg() function

► Syntax:

AVG(set [, numeric expression])

Notes:

Evaluates a set and returns the average of the non-empty values of the cells in the set, averaged over the measures in the set or over a specified measure.

► Example: show the average profit for each calendar year

WITH MEMBER [Measures].[Avg Profit] AS
      AVG( [Time].[Calendar].[Month].MEMBERS, Measures.[Profit])
SELECT
      Measures.[Avg Profit] ON COLUMNS,
      [Time].[Calendar].[Year].MEMBERS ON ROWS
FROM  [Sales]

Sum() function

► Syntax:

SUM(set [, numeric expression])

Notes:

Shows the sum of all values of the cells in the set.

► Example: show the total profit for the calendar years 2013 and 2014 (the sum of the 2013 and 2014 profit values for all members of the [Product].[Category] hierarchy)

WITH MEMBER [Measures].[Sum Profit] AS 
        SUM( { [Time].[Calendar].[Year].[2013],
               [Time].[Calendar].[Year].[2014] }
           , [Measures].[Profit]
           )
SELECT [Measures].[Sum Profit] ON COLUMNS,
         [Product].[Category].Members ON ROWS
FROM [Sales]

StdDev() function

► Syntax:

STDDEV(set [, numeric expression])

Notes:

Returns the sample standard deviation of a numeric expression evaluated over a set, using the unbiased population formula (dividing by n-1).

► Example: show the standard deviation of the profit values, evaluated over the first 3 months of calendar year 2014, using the unbiased population formula

WITH MEMBER [Measures].[Stddev Profit] AS 
	STDDEV( { [Time].[Calendar].[Month].[January 2014],
               [Time].[Calendar].[Month].[February 2014],
               [Time].[Calendar].[Month].[March 2014]}
           , [Measures].[Profit]
	   )

PeriodsToDate() function

► Syntax:

PERIODSTODATE([level [, member]])

Notes:

Returns a set of sibling members from the same level as a given member, starting with the first sibling and ending with the given member, as constrained by a specified level in the Time dimension.
If the member expression is missing, then the level hierarchy.CurrentMember will be used.
If the level expression is missing, then the parent level of the [Time].CurrentMember in the default time dimension of the cube will be used.

► Example: aggregate profit for the first 8 calendar months of the year 2014

WITH MEMBER [Time].[Calendar].[first8Months2014] AS
    AGGREGATE (
        PERIODSTODATE(  [Time].[Calendar].[Year], 
                        [Time].[Calendar].[Month].[August 2014],
                     )
              )		
SELECT [Time].[Calendar].[All sales] ON COLUMNS,
    [Store].[Store City].MEMBERS ON ROWS
FROM [Sales]
WHERE [Measures].[Profit]

YTD() function

► Syntax:

YTD([member])

Notes:

Returns a set of sibling members from the same level as a given member, starting with the first sibling and ending with the given member, as constrained by the Year level in the Time dimension.
Ytd(member) is equivalent to PeriodsToDate(Year level, member).
If no member is defined, then [Time].currentMember is used.
This function does not work when the Type property is set to FiscalYears.

► Example: aggregate profit for the first 8 calendar months of the year 2014

WITH MEMBER [Time].[Calendar].[first8Months2014] AS
    AGGREGATE (
                YTD( [Time].[Calendar].[Month].[August 2014] )
              )		
SELECT [Time].[Calendar].[All sales] ON COLUMNS,
    [Store].[Store City].MEMBERS ON ROWS
FROM [Sales]
WHERE [Measures].[Profit]

CrossJoin() function

► Syntax:

CROSSJOIN(set1, set2)

Notes:

Returns the cross product of one or more sets.

► Example: the query axis (ROWS) is the combination of 2 cube dimensions

SELECT [Time].[Calendar].[Year].[2014].CHILDREN ON COLUMNS,
    CROSSJOIN( [Store].[Store State].MEMBERS,
                [Product].[Product Family].MEMBERS
             ) ON ROWS
FROM [Sales]
WHERE ([Measures].[Profit])

NonEmpty() function

► Syntax:

NONEMPTY(set1 [,set2])

Notes:

Returns the set of non-empty tuples from a specified set, based on the cross product of the specified set with a second set.

► Example:

SELECT [Time].[Calendar].[Year].[2014].CHILDREN ON COLUMNS,
    NONEMPTY(
        CROSSJOIN( [Store].[Store State].MEMBERS,
                   [Product].[Product Family].MEMBERS
                 )
            ) ON ROWS
FROM [Sales]
WHERE ([Measures].[Profit])

NonEmptyCrossJoin() function

► Syntax:

NONEMPTYCROSSJOIN(set1 [,set2, . . .][, count])

Notes:

Returns a set that contains the cross product of one or more sets, excluding empty tuples and tuples without associated fact table data.
If count is not specified, the function cross-joins all specified sets and excludes empty members from the result set.
If a number of sets is specified, the function cross joins the numbers of sets specified, starting with the first specified set.

► Example:

SELECT [Time].[Calendar].[Year].[2014].CHILDREN ON COLUMNS,
    NONEMPTYCROSSJOIN( [Store].[Store State].MEMBERS,
                       [Product].[Product Family].MEMBERS
                     ) ON ROWS
FROM [Sales]
WHERE ([Measures].[Profit])

Except() function

► Syntax:

EXCEPT(set1 ,set2 [, ALL])

Notes:

Evaluates two sets and removes those tuples in the first set that also exist in the second set; ensuing duplicates are optionally removed.
If ALL is specified, the function retains duplicates found in the first set; duplicates found in the second set will still be removed. The members are returned in the order they appear in the first set.

► Example:

SELECT [Time].[Calendar].[Month].CHILDREN ON COLUMNS,
    EXCEPT( [Product].[Product Categories].[All].Children ,
            {[Product].[Product Categories].[Components]}
          ) ON ROWS
FROM [Sales]
WHERE ([Measures].[Profit])

Jboss JBPM 5.4

jBPM is a flexible Business Process Management (BPM) Suite. It allows you to model, execute and monitor business processes, throughout their life cycle.

jBPM focuses on executable business process, which are business processes that contain enough detail so they can actually be executed on a BPM engine. Executable business processes bridge the gap between business users and developers as they are higher-level and use domain-specific concepts that are understood by business users but can also be executed directly.

To interact with the process engine (for example, to start a process), you need to set up a session. This session will be used to communicate with the process engine. A session needs to have a reference to a knowledge base, which contains a reference to all the relevant process definitions. This knowledge base is used to look up the process definitions whenever necessary. To create a session, you first need to create a knowledge base, load all the necessary process definitions (this can be from various sources, like from classpath, file system or process repository) and then instantiate a session.

For example, imagine you are writing an application to process sales orders. You could then define one or more process definitions that define how the order should be processed. When starting up your application, you first need to create a knowledge base that contains those process definitions. You can then create a session based on this knowledge base so that, whenever a new sales order comes in, a new process instance is started for that sales order. That process instance contains the state of the process for that specific sales request.

The jBPM project has a clear separation between the API the users should be interacting with and the actual implementation classes. The public API exposes most of the features we believe "normal" users can safely use and should remain rather stable across releases. Expert users can still access internal classes but should be aware that they should know what they are doing and that the internal API might still change in the future.

As explained above, the jBPM API should thus be used to create a knowledge base that contains your process definitions, and to create a session to start new process instances, signal existing ones, register listeners, etc.

More information: http://docs.jboss.org/jbpm/v5.4/userguide/

Datamart DSL

Contents

Copyright Notice

Datamart DSL

Datamart definition

entity

navigation

many to one … to entity …

one to many … to entity …

properties

on axis rows/columns

aggregate

scale

conditions

selected

filtered

filler rows mm to nn

cube

Keywords

nonempty values

derive … from

axis

slicer

Definitions

Cube Measure

Cube Derived Measure

Cube Hierarchy

default

condensed

exploded

level ... filtered

level ... selected

detailed

except

order by ... (descending)

Cube Member Tuple

Cube Set Tuple

Cube Aggregation

CONTINUE HERE

The Basics

MDX (MultiDimensional eXpressions)

Data types

Basic Syntax of an MDX query

Examples

A typical query

.MEMBERS and .CHILDREN functions

Calculate Member

Slicer Dimension

Filter() function

Order() function

Lag() function

Lead() function

PrevMember function

NextMember function

FirstChild function

LastChild function

Head() function

Tail() function

TopCount() function

BottomCount() function

TopPercent() function

BottomPercent() function

TopSum() function

BottomSum() function

Aggregate() function

Avg() function

Sum() function

StdDev() function

PeriodsToDate() function

YTD() function

CrossJoin() function

NonEmpty() function

NonEmptyCrossJoin() function

Except() function

Jboss JBPM 5.4

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