Multidimensional Data Model

What is a information regular hexahedron?

A information regular hexahedron allows informations to be modeled and viewed in multiple dimensions. It is defined by dimensions and facts. In general footings. dimensions are the positions or entities with regard to which an organisation wants to maintain records. Each dimension may hold a tabular array associated with it. called a dimension tabular array. which farther describes the dimension. Facts are numerical steps. The fact tabular array contains the names of the facts. or steps. every bit good as keys to each of the related dimension tabular arraies.

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2-D representation. the gross revenues for Vancouver are shown with regard to the clip dimension ( organized in quarters ) and the point dimension ( organized harmonizing to the types of points sold ) . The fact. or step displayed is dollars sold.

Now. say that we would wish to see the gross revenues informations with a 3rd dimension. For case. suppose we would wish to see the informations harmonizing to clip. point. every bit good as location. The above tabular arraies show the informations at different grades of summarisation. In the information repositing research literature. a information regular hexahedron such as each of the above is referred to as a cuboid. Give a set of dimensions. we can build a lattice of cuboids. each demoing the information at a different degree of summarisation. or group by ( i. e. . summarized by a different subset of the dimensions ) . The lattice of cuboids is so referred to as a information regular hexahedron. The undermentioned figure shows a lattice of cuboids organizing a information regular hexahedron for the dimensions clip. point. location. and provider.

The cuboid which holds the lowest degree of summarisation is called the base cuboid. The 0-D cuboid which holds the highest degree of summarisation is called the vertex cuboid. The apex cuboid is typically denoted by all.


The entity-relationship informations theoretical account is normally used in the design of relational databases. where a database scheme consists of a set of entities or objects. and the relationships between them. Such a information theoretical account is appropriate for online dealing processing. Data warehouses. nevertheless. necessitate a concise. subject-oriented scheme which facilitates online informations analysis. The most popular informations theoretical account for information warehouses is a multidimensional theoretical account. This theoretical account can be in the signifier of a star scheme. a snow flake scheme. or a fact configuration scheme.

Star scheme:

The star scheme is a patterning paradigm in which the information warehouse contains ( 1 ) a big cardinal tabular array ( fact tabular array ) . and ( 2 ) a set of smaller attender tabular arraies ( dimension tabular arraies ) . one for each dimension. The scheme graph resembles a starburst. with the dimension tables displayed in a radial form around the cardinal fact tabular array.

In Star Schema. each dimension is represented by merely one tabular array. and each tabular array contains a set of properties. For illustration. the location dimension tabular array contains the property set { location_key. metropolis. province. state } This restraint may present some redundancy.

Example: Chennai. Madurai is both metropoliss in the TamilNadu province in India.

Snow Flake scheme:

The Snow Flake scheme is a discrepancy of the star scheme theoretical account. where some dimension tabular arraies are normalized. thereby further dividing the information into extra tabular arraies. The ensuing schema graph forms a form similar to a snow flake.

Snowflake scheme of a information warehouse for gross revenuesThe major difference between the snowflake and star scheme theoretical accounts is that the dimension tabular arraies of the snowflake theoretical account may be kept in normalized signifier to cut down redundancies. Such a tabular array is easy to keep and besides saves storage infinite

Drawback:The Snowflake scheme needs more articulations will be needed to put to death a question. so it is non popular as the Star Schema in Data Warehouse Design. A via media between the star scheme and the snowflake scheme is to follow a assorted scheme where merely the really big dimension tabular arraies are normalized.

Fact configuration:Sophisticated applications may necessitate multiple fact tabular arraies to portion dimension tabular arraies. This sort of scheme can be viewed as a aggregation of stars. and hence is called a galaxy scheme or a fact configuration.

Fact configuration scheme of a information warehouse for gross revenues and transportation

This schema species two fact tabular arraies. gross revenues and transportation. The gross revenues table definition is indistinguishable to that of the star scheme. A fact configuration scheme allows dimension tabular arraies to be shared between fact tabular arraies. In informations warehousing. there is a differentiation between a information warehouse and a data marketplace. A information warehouse collects information about topics that span the full organisation. such as clients. points. gross revenues. assets. and forces. and therefore its range is enterprise-wide.

For informations warehouses. the fact configuration scheme are normally used since it can pattern multiple. interconnected topics. A data marketplace. on the other manus. is a section subset of the informations warehouse that focuses on selected topics. and therefore its range is department-wide. For informations marketplaces. the star or snowflake schemes are popular since each are geared towards patterning individual topics. Examples for specifying star. snowflake. and fact configuration scheme In DMQL. The following are the sentence structure to specify the Star. Snowflake. and Fact configuration Schemas:


A step value is computed for a given point by aggregating the information corresponding to the several dimension-value braces specifying the given point. Measures can be organized into three classs:

1. Distributive Measure2. Algebraic Measure3. Holistic MeasureBased on the sort of aggregative maps are used.

1. Distributive Measure

An aggregative map is distributive if it can be computed in a distributed mode as follows: Suppose the information is partitioned into n sets. The calculation of the map on each divider derives one sum value. If the consequence derived by using the map to the n sum values is the same as that derived by using the map on all the informations without partitioning. the map can be computed in a distributed mode. For illustration. count ( ) can be computed for a information regular hexahedron by first partitioning the regular hexahedron into a set of subcubes. calculating count ( ) for each subcube. and so summing up the counts obtained for each subcube. Hence count ( ) is a distributive sum map. For the same ground. amount ( ) . min ( ) . and soap ( ) are distributive aggregative maps. A step is distributive if it is obtained by using a distributive sum map.

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Multidimensional Data Model. (2017, Aug 19). Retrieved from