Essay On Various Tools Of Olap Operations

SURVEY ON VARIOUS TOOLS OF OLAP OPERATIONS

By

VIVEK KUMAR

12BCE112

DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING

Ahmedabad 382481

Survey on various tools of olap operations

Seminar

Submitted in fulfillment of the requirements

For the degree of

Bachelor of Technology in Computer Engineering/Information Technology

By

VIVEK KUMAR

12BCE112

Guided By

DVIJESH BHATT

[DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING]

DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING

Ahmedabad 382481

CERTIFICATE

This is to certify that the Seminar entitled ” SURVEY ON VARIOUS TOOLS OF OLAP OPERATIONS ” submitted by VIVEK KUMAR (12BCE112) , towards the partial fulfillment of the requirements for the degree of Bachelor

Consider the following diagram that shows how slice works. Here “time” will perform the slicing dimension using the criterion time = "Q1".

1.2.2 Dice

Two of more dimensions are selected by the Dice to form a given cube and provides a new sub-cube. Consider the following diagram that shows the dice operation. 1.2.3 Drill Down/Up Drilling down or up is a unique analytical technique in which the user travels among levels of data ranging to the most detailed (down) from the most summarized (up). 1.2.4 Roll-up One or more dimensions of all the data relationships is being computed by a roll-up operation. To do this, a computational relationship or formula might be defined. 1.2.5 Pivot

To change the dimensional orientation of a report or page display.

1.3 DATAWAREHOUSE

A data warehouse is a repository of an organization's electronically stored data. A data warehouse is a

• subject-oriented

• integrated

• time-varying

• non-volatile

The part and parcel components of a data warehousing system are the means to:

• Retrieve & Analyze the data

• Extract, Transform & Load the data.

• Manage the data

• Limited by SQL functionalities: Because ROLAP technology mainly relies on generating SQL statements to query the relational database, and SQL statements do not fit all needs (for example, it is difficult to perform complex calculations using SQL), ROLAP technologies are therefore traditionally limited by what SQL can do. ROLAP vendors have mitigated this risk by building into the tool out-of-the-box complex functions as well as the ability to allow users to define their own functions

MOLAP

• Limited in the amount of data it can handle: Because all calculations are performed when the cube is built, it is not possible to include a large amount of data in the cube itself. This is not to say that the data in the cube cannot be derived from a large amount of data. Indeed, this is possible. But in this case, only summary-level information will be included in the cube itself.

• Requires additional investment: Cube technology are often proprietary and do not already exist in the organization. Therefore, to adopt MOLAP technology, chances are additional investments in human and capital resources are