This document provides an introduction to data mining and big data. It defines data mining as the process of analyzing data from different perspectives to discover useful patterns and relationships. The document lists some common applications of data mining in industries like finance, insurance, and telecommunications. It also outlines the typical steps involved in data mining, including data integration, cleaning, transformation, and knowledge presentation. Big data is defined as extremely large data sets that are difficult to process using traditional tools. The rapid growth of data from sources like social media and mobile devices is driving the need for tools to handle big data's volume, velocity, and variety of data types.

1. Submitted to:
Mahabub Haque(Sohel)
Lecture,
Dept. Of CSE
University Of Chittagong
Submitted by:
Aseem Chakrabarthy

2. THESIS STATEMENT
DATA MINING AND BIG DATA

3. Preliminary Knowledge about Data mining & Big Data
Data Mining
1.What is Data Mining?
2.Why Data Mining?
3.Application Of Data Mining
4.Steps Of Data Mining
5.Data Warehousing
6.Data Mining Tools
Big Data
1.What is Big Data?
2.Why Big Data?
3.What Type Of Data is “Big Data”?
4. Problems Associated with “BIG DATA”
5.Characteristics of Big Data.

4. What is Data Mining?
Data mining (sometimes called data or knowledge discovery) is the
process of analyzing data from different perspectives and
summarizing it into useful information - information that can be used
to increase revenue, cuts costs, or both. Data mining software is one
of a number of analytical tools for analyzing data. It allows users to
analyze data from many different dimensions or angles, categorize it,
and summarize the relationships identified. Technically, data mining is
the process of finding correlations or patterns among dozens of fields
in large relational databases.

5. Why Data Mining?
=>I can’t find the data I need
 I can’t get the data I need
 I can’t understand the data I found
 I can’t use the data I found
 Data explosion problem:
Advance data collection tools and database technology lead to
tremendous amounts of data stored in databases.

6. Application Of Data Mining
Industry Application
Finance Credit card Analysis
Insurance Claims , fraud Analysis
Telecommunication Call record analysis
Transport logistics management
Consumer goods promotion analysis
Scientific Research Image , Video , Speech
Utilities Power usage analysis

7. Steps Of Data Mining
 Data integration
 Data selection
 Data cleaning
 Data transformation
 Data mining
 Pattern evaluation
 Knowledge presentation

8. Data Warehousing:
 A data warehouse is a subject-oriented ,integrated ,
time-variant and non-volatile collection of data in
support of management’s decision making process.
 Data warehousing is the process of constructing and
using a data warehouse. A data warehouse is
constructed by integrating data from multiple
heterogeneous sources that support analytical
reporting, structured and/or ad hoc queries, and
decision making. Data warehousing involves data
cleaning, data integration, and data consolidations.

9. Data Mining Tools:
 Microsoft SQL server 2005
 Microsoft SQL Server 2008
 Oracle Data Mining
 DBMiner

10. What is big Data?
=> ‘Big Data’ is similar to ‘small data’ but bigger in size.
Big data is a term used to represent a collection of data sets which are
extremely large in size , because it is too large and complex . It is difficult to
process using tradition tools
=>Walmart handles more than 1 million customer transactions every hour.
=>Facebook handles 40 billion photos from its user base

11. Why Big Data?
=>Growth Of Big Data is needed
Increase of storage capacities
Increase of processing power
Availability of data(Different data types)
Every day we create 2.5 quintillion bytes of data; 90% of the data in the
world today has been created in the last two years alone.
=> Facebook generate 10TB daily
=>Twitter generates 7TB of data daily
=>IBM claims 90% of today’s stored data was generated in just the last
two years.

12. What Type Of Data is “Big Data”?
=>Social networking habits
=>Location of cell phone usage
 Shopping habits
 Search habit
 Online Interests
 Many , Many more

13. 4.Problems Associated with “BIG DATA”
=>Physical problems
 Processing problems
=>Big business VS small business
=>Incorrect Data

14. Characteristics Of Big Data:
=>Three Characteristics of Big Data V3s
->Volume (Data Quantity)
->Velocity (Data Speed)
->variety(Data Types)

15. Thank you