Olap and Data Mining

MIS636 B (Team E)

STUDENT NAME: Juhi Udani

1. What are the distinguishing characteristics of OLAP and data mining analyses? Compare and contrast OLTP and OLAP queries.

OLAP and data mining are used to solve various types of analytic problems:

• OLAP give a brief statement data and makes forecasts. For example, OLAP answers questions like "What are the average sales of the particular items?”

• Data mining identify hidden patterns in data. It works operates at a detail level instead of a summary level. Data mining answers questions like "Who is likely to buy a particular product in store in upcoming week and what are the characteristics of these buyers?"

Data mining and OLAP, these two can supplement each other. For example, OLAP might pinpoint problems with sales of products of store in a certain region. While Data mining could be used to gain insight detail about the behavior of individual customers in the region. Finally, OLAP can be used to track the net sales while data mining predicts something like a 5% increase in sales.

OLTP -Online Transaction Processing is characterized by a large number of short on line transactions like Insert, Update and Delete. It is a very important because it is put on maintaining data integrity in multi-access, very fast query processing.

environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model usually 3NF.

OLAP -On-line Analytical Processing is defined by relatively less volume of transactions. Queries are often involving aggregations and very complex in OLAP response time is an effectiveness measure in OLAP system. In OLAP applications, Data Mining techniques widely used. In OLAP database there is historical, aggregated data which is stored in multi dimensional schemas usually star schema.

1. Describe normalization. Include in your answer a discussion of redundancy and dependencies and how it relates to normalization.

With the help of the normalization, with the surety of data integrity and eliminating data redundancy we can reduce data unto a set of relations.

• Data integrity means each and every data of the database are consistent and all integrity constraints are satisfied.
• Data redundancy – the data is said to contain redundancy if data in the database can be gained in two different locations which is called direct redundancy and also data can be found from other data items which is indirect redundancy.

Data should be stored only one time and avoid storing data that can be calculated from other data already held in the database. While doing Normalization process redundancy must be discarded, but data integrity rules should not be violated. Problems can arise If redundancy exists in the database, then when the database is in normal operation.

• When data is added the data must be added correctly in all tables where there is redundancy. For example, in a database there are two table and both tables contain the name of customers, then creating a new entry requires that both tables be updated with the r name of customers.
• When data is updated in the database, if the data being changed has redundancy, then all version of redundant data must be updated simultaneously. So in the customer example a change to the customer name must happen in all tables simultaneously.

The removal of redundancy helps to prevent errors of insertion, deletion, and update since the data is only available in one attribute of one table in the database.