Data Warehousing - Multidimensional OLAP

Introduction

Multidimensional OLAP (MOLAP) uses the array-based multidimensional storage engines for multidimensional views of data. With multidimensional data stores, the storage utilization may be low if the data set is sparse. Therefore many MOLAP Server uses the two level of data storage representation to handle dense and sparse data sets.

Points to remember:

·         MOLAP tools need to process information with consistent response time regardless of level of summarizing or calculations selected.

·         The MOLAP tools need to avoid many of the complexities of creating a relational database to store data for analysis.

·         The MOLAP tools need fastest possible performance.

·         MOLAP Server adopts two level of storage representation to handle dense and sparse data sets.

·         Denser subcubes are identified and stored as array structure.

·         Sparse subcubes employs compression technology.

MOLAP Architecture

MOLAP includes the following components.

·         Database server

·         MOLAP server

·         Front end tool

Advantages

Here is the list of advantages of Multidimensional OLAP

·         MOLAP allows fastest indexing to the precomputed summarized data.

·         Helps the user who are connected to a network and need to analyze larger, less defined data.

·         Easier to use therefore MOLAP is best suitable for inexperienced user.

Disadvantages

·         MOLAP are not capable of containing detailed data.

·         The storage utilization may be low if the data set is sparse.

MOLAP vs ROLAP

SN	MOLAP	ROLAP
1	The information retrieval is fast.	Information retrieval is comparatively slow.
2	It uses the sparse array to store the data sets.	It uses relational table.
3	MOLAP is best suited for inexperienced users since it is very easy to use.	ROLAP is best suited for experienced users.
4	The separate database for data cube.	It may not require space other than available in Data warehouse.
5	DBMS facility is weak.	DBMS facility is strong.

Data Warehousing - Schemas

Introduction

The schema is a logical description of the entire database. The schema includes the name and description of records of all record types including all associated data-items and aggregates. Likewise the database the data warehouse also require the schema. The database uses the relational model on the other hand the data warehouse uses the Stars, snowflake and fact constellation schema. In this chapter we will discuss the schemas used in data warehouse.

Star Schema

·         In star schema each dimension is represented with only one dimension table.

·         This dimension table contains the set of attributes.

·         In the following diagram we have shown the sales data of a company with respect to the four dimensions namely, time, item, branch and location.

·         There is a fact table at the centre. This fact table contains the keys to each of four dimensions.

·         The fact table also contain the attributes namely, dollars sold and units sold.

Note: Each dimension has only one dimension table and each table holds a set of attributes. For example the location dimension table contains the attribute set {location_key,street,city,province_or_state,country}. This constraint may cause data redundancy. For example the "Vancouver" and "Victoria" both cities are both in Canadian province of British Columbia. The entries for such cities may cause data redundancy along the attributes province_or_state and country.

Snowflake Schema

·         In Snowflake schema some dimension tables are normalized.

·         The normalization split up the data into additional tables.

·         Unlike Star schema the dimensions table in snowflake schema are normalized for example the item dimension table in star schema is normalized and split into two dimension tables namely, item and supplier table.

·         Therefore now the item dimension table contains the attributes item_key, item_name, type, brand, and supplier-key.

·         The supplier key is linked to supplier dimension table. The supplier dimension table contains the attributes supplier_key, and supplier_type.

<b<>Note: Due to normalization in Snowflake schema the redundancy is reduced therefore it becomes easy to maintain and save storage space.</b<>

Fact Constellation Schema

·         In fact Constellation there are multiple fact tables. This schema is also known as galaxy schema.

·         In the following diagram we have two fact tables namely, sales and shipping.

·         The sale fact table is same as that in star schema.

·         The shipping fact table has the five dimensions namely, item_key, time_key, shipper-key, from-location.

·         The shipping fact table also contains two measures namely, dollars sold and units sold.

·         It is also possible for dimension table to share between fact tables. For example time, item and location dimension tables are shared between sales and shipping fact table.

Schema Definition

The Multidimensional schema is defined using Data Mining Query Language( DMQL). the two primitives namely, cube definition and dimension definition can be used for defining the Data warehouses and data marts.

SYNTAX FOR CUBE DEFINITION

define cube < cube_name > [ < dimension-list > }: < measure_list >

SYNTAX FOR DIMENSION DEFINITION

define dimension < dimension_name > as ( < attribute_or_dimension_list > )

Star Schema Definition

The star schema that we have discussed can be defined using the Data Mining Query Language (DMQL) as follows:

define cube sales star [time, item, branch, location]:   

dollars sold = sum(sales in dollars), units sold = count(*)             

define dimension time as (time key, day, day of week, month, quarter, year)

define dimension item as (item key, item name, brand, type, supplier type)             

define dimension branch as (branch key, branch name, branch type)                      

define dimension location as (location key, street, city, province or state, country)

Snowflake Schema Definition

The Snowflake schema that we have discussed can be defined using the Data Mining Query Language (DMQL) as follows:

define cube sales snowflake [time, item, branch, location]:

dollars sold = sum(sales in dollars), units sold = count(*)

define dimension time as (time key, day, day of week, month, quarter, year)

define dimension item as (item key, item name, brand, type, supplier

(supplier key, supplier type))

define dimension branch as (branch key, branch name, branch type)

define dimension location as (location key, street, city

(city key, city, province or state, country))

Fact Constellation Schema Definition

The Snowflake schema that we have discussed can be defined using the Data Mining Query Language (DMQL) as follows:

define cube sales [time, item, branch, location]:

dollars sold = sum(sales in dollars), units sold = count(*)

define dimension time as (time key, day, day of week, month, quarter, year)

define dimension item as (item key, item name, brand, type, supplier type)

define dimension branch as (branch key, branch name, branch type)

define dimension location as (location key, street, city, province or state,country)

define cube shipping [time, item, shipper, from location, to location]:

dollars cost = sum(cost in dollars), units shipped = count(*)

define dimension time as time in cube sales

define dimension item as item in cube sales

define dimension shipper as (shipper key, shipper name, location as

location in cube sales, shipper type)

define dimension from location as location in cube sales

define dimension to location as location in cube sales