This document discusses big data mining. It defines big data as large volumes of structured and unstructured data that are difficult to process using traditional methods due to their size. It describes the characteristics of big data including volume, variety, velocity, variability, and complexity. It also discusses challenges of big data such as data location, volume, hardware resources, and privacy. Popular tools for big data mining include Hadoop, Apache S4, Storm, Apache Mahout, and MOA. Hadoop is an open source software framework that allows distributed processing of large datasets across clusters of computers. Common algorithms for big data mining operate at the model and knowledge levels to discover patterns and correlations across distributed data sources.

1. Big Data Mining

2. Overview
 Introduction
 Characteristics of Big Data
 Big Data and it’s challenges
 Big Data mining Tools
 Big Data mining algorithm
 Applications of Big Data
 References
 Q&A

3. Introduction

4. Interesting Facts
 The volume of business data worldwide, across all companies, doubles every
1.2 years (was 1.5 years)
 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
 In the last years cost of storage and processing power dropped significantly
 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)

5. What is

6. The term Big data is used to describe a massive
volume of both structured and unstructured data
that is so large that it's difficult to process using
traditional database and software techniques.
-Webopedia

7. Characteristics of Big Data
Volume - The quantity of data
 Variety - categorizing the data
 Velocity - speed of generation of data or the speed of processing
the data
 Variability - Inconsistency
 Complexity - Managing the data

8. 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

9.  Challenges
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.
Hardware resources- RAM capacity
Privacy- Medical reports, bank transactions
Having domain knowledge
Getting meaningful information
 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

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

11. 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

13. 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.

14.  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

15. 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.

16. References
[1] IEEE, Data Mining with Big Data, January 2014
[2] McKinsy Global Institute, Big Data: The next frontier for innovation, competition and
productivity- May 2011
[3] Xindong Wu, Xinguan Zhu, Gong-Qing Wu, Wei Ding, 2013, Data Mining with Big Data
[4] Ahmed and Karypis 2012, Rezwan Ahmed, George Karpis, Algorithms for mining the evolution
of conserved relational states in dynamic network
[5] Wu X. 2000, Building Intelligent Learning Database Systems, AI Magazine
[6] Oracle, June 2013,Unstructured Data Management with Oracle Database 12c
[7] Valery A.Petrushin, Jia-Yu Pan, Cees G.M.Snoek, 2010,Tenth International Workshop on
Multimedia Data Mining
[8] Big data[Online].Available:www.en.wikipedia.org/wiki/Big_data
[9] Big data [Online]. Available: www.webopedia.com/TERM /B/ big_data.html
[10]IBM big data and information management [Online]. Available: www-
01.ibm.com/software/data/bigdata
[11] Big data [Online]. Available: www.adainbigdata.com
[12] Big Data Explained [Online]. Available: www.mongodb.com/big-data-explained
[13] Big data [Online]. Available: www.sas.com/en_us/insights/big-data/what-is-big-data.html
[14] Big Data Mining Tools[Online]. Available: www.albertbifet.com/big-data-mining-tools

19. Cloud storage for Big Data
Processing