Presentation on

DATA-WAREHOUSE OLAP, OLAM and Cluster Analysis while using PARTITION METHOD.

Presented byRAVI SINGH SHEKHAWATK11562BCA 6th SEMESTER

CONTENTSWhat is a DATA-WAREHOUSE?What is a DATA-WAREHOUSE?

Flaws in DATA WAREHOUSE SystemFlaws in DATA WAREHOUSE System

DATA-WAREHOUSE v/s Regular DATBASEDATA-WAREHOUSE v/s Regular DATBASE

What is OLAP?What is OLAP?

Catagorisation of OLAPCatagorisation of OLAP

SLICE & DICESLICE & DICE

What is OLAM?What is OLAM?

Cluster analysis using Partitioning Method

What is DATA

WAREHOUSE?

A data warehouse is a copy of transaction data specifically

structured for query and analysis.

Simple Defination

In DepthA data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process.

Subject-Oriented: A data warehouse can be used to analyze a particular subject area. For example, "sales" can be a particular subject.

Integrated: A data warehouse integrates data from multiple data sources. For example, source A and source B may have different ways of identifying a product, but in a data warehouse, there will be only a single way of identifying a product.

Time-Variant: Historical data is kept in a data warehouse. For example, one can retrieve data from 3 months, 6 months, 12 months, or even older data from a data warehouse. This contrasts with a transactions system, where often only the most recent data is kept. For example, a transaction system may hold the most recent address of a customer, where a data warehouse can hold all addresses associated with a customer.

Non-volatile: Once data is in the data warehouse, it will not change. So, historical data in a data warehouse should never be altered.

Flaws in DATA

WAREHOUSESystems.

Data warehouses are expensive to scale, and do not excel at handling raw, unstructured, or complex data. However, data warehouses are still an important tool in the big data era.

DATAWAREHOUSE

V/SRegular

Database

Opretional databases are optimized to maintain strict accuracy of data in the

moment by rapidly updating real-time data.

Data warehouses, by contrast, are designed to give a long-range view of

data over time. They trade off transaction volume and instead specialize in data aggregation.

What is OLAP?

Software tools that provides analysis of data stored in a database.

OLAP tools enable users to analyze different dimensions of multidimensional data.

The chief component of OLAP is the OLAP server, which sits between a client

and a database management systems (DBMS).

Catagorisation of OLAP

In the OLAP world, there are mainly two different types:

● Multidimensional OLAP (MOLAP)

● Relational OLAP (ROLAP).

● Hybrid OLAP (HOLAP) refers to technologies that combine MOLAP and ROLAP.

MOLAP This is the more traditional way of OLAP analysis. In MOLAP, data is stored in a multidimensional cube. The storage is not in the relational

database, but in proprietary formats.

ROLAP This methodology relies on

manipulating the data stored in the relational database to give the

appearance of traditional OLAP's slicing and dicing functionality.

SLICE AND DICE

HOLAP HOLAP technologies attempt to

combine the advantages of MOLAP and ROLAP. For summary-type

information, HOLAP leverages cube technology for faster performance. When detail information is needed, HOLAP can "drill through" from the cube into the underlying relational

data.

What is OLAM?

An Integration of Data Mining and Data Warehousing.

Online Analytical Mining integrates with Online Analytical Processing with data

mining and mining knowledge in multidimensional databases.

In Depth

High quality of data in data warehouses.

Available information processing infrastructure surrounding data warehouses.

OLAP−based exploratory data.

AnalysisOnline selection of data mining functions.

Cluster analysis using Partitioning

Method

Suppose we are given a database of ‘n’ objects and the partitioning method constructs ‘k’ partition of data.Each partition will represent a cluster and k ≤ n. It means that it will classify the data into k groups, which satisfy the following rules−

1.Each group contains at least one object.

2.Each object must belong to exactly one group.

Procedure−

For a given number of partitions (say k), the partitioning method will create an initial partitioning.

Then it uses the iterative relocation technique to improve the partitioning by moving objects from one group to other.