! " # $ Online Analytical Processing (OLAP) is an industryaccepted. analysis and easy reporting on large volumes of data, represented as cubes.

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1 Cláudia Antunes 006 Cubos um cubo é a estrutura de dados usada para manipular os dados que instanciam um modelo multidimensional as arestas do cubo correspondem às diferentes dimensões a intersecção dos valores de cada dimensão (células) expressam a agregação dos factos registados na combinação dessas condições Célula: montante de vendas para os s vendidos no D em NY D D Dn Cubos os cubos são criados para pré-calcular as agregações mais frequentes, para acelerar a exploração de dados existem vários cubos para representar um só modelo O cubo que representa os factos ao mais baixo nível de detalhe designa-se por cubo base O cubo ao mais alto nível de detalhe por cubo apíce Portugal Agregação: montante de vendas para todos os produtos vendidos no ano 989 em Portugal USA Célula 4 When you create and use summarized tables, application performance can be substantially improved. The summarized tables eliminate the need to run summaries at execution time. The presummarized data must be refreshed only when the underlying data changes. Every time that the user moves in the application a new summarization must often be calculated. However, each summarization could be stored within a cube, which eliminates the processing time of calculating every type of summarization.! " # $ Online Analytical Processing (OLAP) is an industryaccepted reporting technology that provides highperformance analysis and easy reporting on large volumes of data, represented as cubes. Goals: to provide fast and flexible access to data summarization, to view trends over time, to look at different relationships in data by looking beyond traditional two-dimensional row and column data analysis. 5 6

2 Roll-up (drill-up) summarize data by climbing up hierarchy or by dimension reduction D D Dn Drill-down (roll down): reverse of roll-up, detailing data from higher level summary to lower level summary or detailed data, or introducing new dimensions D D Dn Roll-up () Drill-down() 7 8 Drill-through: Detailing data from higher level summary to lower level summary in a single member Jan89 Feb89 Dec Slice: Select portions of data in a single dimension D D Dn D D Dn Drill-through(989) Slice(=) 9 0 Dice: Select portions of data in conjunction of dimensions D D Dn Dice(= && =D) D Pivot: Visualization of the hyper-dimensional cube in D planes D D Dn Pivoting New York New York Paris Paris

3 % &! " $ The Multidimensional expressions (MDX) language is a standardized, high-level language that is used for querying multidimensional data sources Is considered the multidimensional equivalent to SQL As SQL returns a subset of two-dimensional data from tables, MDX returns a subset of multidimensional data from cubes 4 Cube Measures Dimensions Private Dimension Hierarchies Levels Members 5 6 [ ] The square brackets encircle cube names, dimension names, hierarchy names, and member names. The dot separates the member names and the dimension names Example of a Member: [Time].[ nd half].[ rd quarter] Tuples Tuples list dimensions and members to identify individual cells, as well as a larger section of cells in the cube. Tuples that encompass more than one cell are called slices. ( ) Parentheses encircle tuples. Example of a slice: [Time].[ nd half].[ rd quarter] 7 8

4 Examples of Tuples A slice: ([Time].[ nd half]) A dice: ( [Time].[ nd half], [Source].[Nonground].[Air] ) Sets A set is an ordered collection of zero, one or more tuples. { } Curly brackets encircle sets. { ([Time].[ st half], [Source].[nonground].[air]), (Time.[nd half], [Source].[nonground].[sea]) } : The colon defines a range in sets { ([Time].[ st half].[ st quarter]) : ([Time].[ nd half].[4 th quarter]) }, The comma separates members within tuples and separates tuples within sets. 9 0 The components that an MDX query needs in order to extract the requested information are a component to specify the column headers a component to specify the row headers a pointer to the cube being accessed. General form of the statement: A statement is used to select the dimensions and members to be returned and is referred to as an axis dimension. An axis dimension is expected to return data for multiple members. The WHERE clause is used to restrict the returned data to specific dimension and member criteria a slicer dimension. A slicer dimension is expected to return data for a single member. General form of the query with the addition of the WHERE clause: WHERE (slicer) Example { ( [Route].[nonground].Members ) } ON COLUMNS, { ( [Time].[st half].members ) } ON ROWS st quarter nd quarter air sea Named Sets A named set is an alias for a defined set. Calculated Members Members that are based on evaluated expressions in MDX computed at runtime based on data that already exists in the cube. Advantages: It helps to clarify the logic Increase the ease of maintenance. Increases the efficiency of query execution because the set is only executed once at the beginning, stored, and then reused each time that it is encountered 4 4

5 Use the WITH keyword as part of the statement to create a named set or a calculated member Creating Named Sets: WITH SET set_name AS 'set' WHERE (slicer) The named set created using the WITH SET statement is available only for that query. Named Set example WITH SET ngmembers AS {([Route].[nonground].Members)} ' [ngmembers] ON COLUMNS, { ( [Time].[st half].members ) } ON ROWS 5 6 Creating Calculated Members: WITH MEMBER member-name AS 'valueexpression' WHERE (slicer) The <member_name> value is the fully qualified name of the calculated member, including the dimension or level to which the calculated member is associated. Calculated Members example WITH MEMBER [Measures].[avgPacks] AS avg([measures].[packages]) { ( [Route].[nonground].Members ) } ON COLUMNS, { ( [Time].[st half].members ) } ON ROWS WHERE [Measures].[avgPacks] 7 8 * * Children: returns the children of a member. Members: returns the set of all members in a dimension, hierarchy, or level. CrossJoin: returns the cross product of two sets. TopCount: returns a specified number of items from the top of a set, optionally ordering the set first. CrossJoin Example CROSSJOIN ({[Time].[st half].members}, {[Route].[nonground].Members}) ON COLUMNS, { ( [Source].[Eastern hemisphere].members ) } ON ROWS 9 0 5

6 * TopCount Example TOOUNT ({[Source].[Eastern hemisphere].members)},, [Measures].[Packages]) ON COLUMNS, {[Time].[st half].members} ON ROWS 6