This presentation introduces big data and data mining. Big data refers to extremely large data sets that cannot be processed by traditional software. Data mining is the process of discovering patterns in large data sets using machine learning, statistics, and database systems. The presentation discusses key facts about the growth of data, examples of big data sources like Facebook and Google, challenges of big data like hardware limitations, and solutions like parallel computing. Common big data mining tools are also introduced, including Hadoop, Apache S4, and Storm. Applications of big data and data mining are highlighted in various domains like healthcare, public sector, and biology.

1. Welcome to Our Presentation
Presentation on
Big Data and Data Mining

2. Introduction

3. Big Data is a term for data sets that are so large
or complex that traditional data processing application
softwareis inadequate to deal with them.

4. Data mining is the computing process of
discovering patterns in large data sets involving
methods at the intersection of machine
learning, statistics, and database systems.

5. Important Info
 Daily 2500 quadrillion of data are produced and more than 90 percentage of data
are produced within past two years.
 A regular person is processing daily more data than a 16th century individual in his
entire life
 The volume of business data worldwide, across all companies, doubles every 1.2
years (was 1.5 years)
 Bad data or poor data quality costs US businesses $600 billion annually
 By 2015, 4.4 million IT jobs globally will be created to support big data (Gartner)
 Facebook processes 10 TB of data every day / Twitter 7 TB
 Google has over 3 million servers processing over 2 trillion searches per year in
2012 (only 22 million in 2000)

6. 4 variants of Big Data
Volume
• Data
Quantity
Velocity
• Data Speed
Variety
• Data Types
Variability
• Inconsistency

7. Big Data Mining Algorithm
 Big data applications have so many sources to gather information.
 If we want to mine data, we need to gather all distributed data to the
centralized site. But it is prohibited because of high data transmission cost and
privacy concerns.
 Most of the mining levels order to achieve the pattern of correlations, or patterns
can be discovered from combined variety of sources.
 The global data mining is done through two steps process.
 Model level
 Knowledge level.
 Each and every local sites use local data to calculate the data statistics and it
share this information in order to achieve global data distribution in their data
level.

8.  In model level it will produce local pattern. This pattern will be produced
after mined local data.
 By sharing these local patterns with other local sites, we can produce a single
global pattern.
 At the knowledge level, model correlation analysis investigates the relevance
between models generated from various data sources to determine how
related the data sources are correlated to each other, and how to form
accurate decisions based on models built from autonomous sources

9. DATA MINING CHALLENGES WITH BIG DATA
 Main challenge for an intelligent database is handling Big data.
The important thing is scaling the large amount of data and
provide solution for these problem by HACE theorem

10. Challenges
Hardware resources- RAM capacity
Location of Big Data sources- Commonly Big Data are
stored in different locations
Volume of the Big Data- size of the Big Data grows
continuously.
Privacy- Medical reports, bank transactions
Having domain knowledge
Getting meaningful information

11. Solutions
Parallel computing programming
An efficient platform for computing will
not have centralized data storage instead of
that platform will be distributed in big scale
storage.
Restricting access to the data

12. BIG Data Mining Tools
 Hadoop
 Apache S4
 Strom
 Apache Mahout
 MOA

13. Hadoop
 It is developed by Apache Software Foundation project and open source
software platform for scalable, distributed computing.
 Apache Hadoop software library is a framework that allows for the distributed
processing of large data sets across clusters of computers using simple
programming models.
 Hadoop provides fast and reliable analysis of both Structured and un
structured data.
 It is designed to scale up from single servers to thousands of machines, each
offering local computation and storage.
 Hadoop uses MapReduce programming model to mine data.
 This MapReduce program is used to separate datasets which are sent as input
into independent subsets. Those are process parallel map task.
 Map() procedure that performs filtering and sorting
 Reduce() procedure that performs a summary operation

14. Applications of Big Data
 Healthcare organizations can achieve better insight into disease trends and
patient treatments.
 Public sector agencies can catch fraud and other threats in real-time.
 Applications of Multimedia data
 To find travelling pattern of travelers
 CC TV camera footage
 Photos and Videos from social network
 Recommender system
 Integration and mining of Bio data from various sources in Biological network
by NSF (National Science Foundation).
 Classifying the Big data stream in run time, by Australian Research council.

15. Advantages
 Fast response
 Extract useful information
 Prediction of required data from large amount of data.
 Savour of better results in the form of visualization.

17. We Are: The Genius
 Gopesh Singha ………………….1519
 Md. Mizanur Rahman ………..…1524
 Kawsar Ahmed ……………….…1531
 Hasan Pervez…………………….1520